Merge branch 'main' into fix/next-release-bugs

2025-05-09 21:48:13 +08:00
parent 9ba630b5e8 a6a8324cdb
commit 572ffcc8be
34 changed files with 1283 additions and 694 deletions
--- a/src/renderer/src/assets/images/apps/n8n.ico
+++ b/src/renderer/src/assets/images/apps/n8n.ico
--- a/src/renderer/src/assets/images/apps/n8n.svg
+++ b/src/renderer/src/assets/images/apps/n8n.svg
@@ -0,0 +1 @@
+<svg height="1em" style="flex:none;line-height:1" viewBox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><title>n8n</title><path clip-rule="evenodd" d="M24 8.4c0 1.325-1.102 2.4-2.462 2.4-1.146 0-2.11-.765-2.384-1.8h-3.436c-.602 0-1.115.424-1.214 1.003l-.101.592a2.38 2.38 0 01-.8 1.405c.412.354.704.844.8 1.405l.1.592A1.222 1.222 0 0015.719 15h.975c.273-1.035 1.237-1.8 2.384-1.8 1.36 0 2.461 1.075 2.461 2.4S20.436 18 19.078 18c-1.147 0-2.11-.765-2.384-1.8h-.975c-1.204 0-2.23-.848-2.428-2.005l-.101-.592a1.222 1.222 0 00-1.214-1.003H10.97c-.308.984-1.246 1.7-2.356 1.7-1.11 0-2.048-.716-2.355-1.7H4.817c-.308.984-1.246 1.7-2.355 1.7C1.102 14.3 0 13.225 0 11.9s1.102-2.4 2.462-2.4c1.183 0 2.172.815 2.408 1.9h1.337c.236-1.085 1.225-1.9 2.408-1.9 1.184 0 2.172.815 2.408 1.9h.952c.601 0 1.115-.424 1.213-1.003l.102-.592c.198-1.157 1.225-2.005 2.428-2.005h3.436c.274-1.035 1.238-1.8 2.384-1.8C22.898 6 24 7.075 24 8.4zm-1.23 0c0 .663-.552 1.2-1.232 1.2-.68 0-1.23-.537-1.23-1.2 0-.663.55-1.2 1.23-1.2.68 0 1.231.537 1.231 1.2zM2.461 13.1c.68 0 1.23-.537 1.23-1.2 0-.663-.55-1.2-1.23-1.2-.68 0-1.231.537-1.231 1.2 0 .663.55 1.2 1.23 1.2zm6.153 0c.68 0 1.231-.537 1.231-1.2 0-.663-.55-1.2-1.23-1.2-.68 0-1.231.537-1.231 1.2 0 .663.55 1.2 1.23 1.2zm10.462 3.7c.68 0 1.23-.537 1.23-1.2 0-.663-.55-1.2-1.23-1.2-.68 0-1.23.537-1.23 1.2 0 .663.55 1.2 1.23 1.2z" fill="#EA4B71" fill-rule="evenodd"></path></svg>
--- a/src/renderer/src/assets/images/apps/paratera.ico
+++ b/src/renderer/src/assets/images/apps/paratera.ico
--- a/src/renderer/src/components/Popups/SelectModelPopup.tsx
+++ b/src/renderer/src/components/Popups/SelectModelPopup.tsx
@@ -59,9 +59,11 @@ const PopupContainer: React.FC<PopupContainerProps> = ({ model, resolve }) => {
  const [pinnedModels, setPinnedModels] = useState<string[]>([])
  const [_focusedItemKey, setFocusedItemKey] = useState<string>('')
  const focusedItemKey = useDeferredValue(_focusedItemKey)
-  const [currentStickyGroup, setCurrentStickyGroup] = useState<FlatListItem | null>(null)
+  const [_stickyGroup, setStickyGroup] = useState<FlatListItem | null>(null)
+  const stickyGroup = useDeferredValue(_stickyGroup)
  const firstGroupRef = useRef<FlatListItem | null>(null)
  const scrollTriggerRef = useRef<ScrollTrigger>('initial')
+  const lastScrollOffsetRef = useRef(0)

  // 当前选中的模型ID
  const currentModelId = model ? getModelUniqId(model) : ''
@@ -220,6 +222,45 @@ const PopupContainer: React.FC<PopupContainerProps> = ({ model, resolve }) => {
    return items
  }, [providers, getFilteredModels, pinnedModels, searchText, t, createModelItem])

+  // 基于滚动位置更新sticky分组标题
+  const updateStickyGroup = useCallback(
+    (scrollOffset?: number) => {
+      if (listItems.length === 0) {
+        setStickyGroup(null)
+        return
+      }
+
+      // 基于滚动位置计算当前可见的第一个项的索引
+      const estimatedIndex = Math.floor((scrollOffset ?? lastScrollOffsetRef.current) / ITEM_HEIGHT)
+
+      // 从该索引向前查找最近的分组标题
+      for (let i = estimatedIndex - 1; i >= 0; i--) {
+        if (i < listItems.length && listItems[i]?.type === 'group') {
+          setStickyGroup(listItems[i])
+          return
+        }
+      }
+
+      // 找不到则使用第一个分组标题
+      setStickyGroup(firstGroupRef.current ?? null)
+    },
+    [listItems]
+  )
+
+  // 在listItems变化时更新sticky group
+  useEffect(() => {
+    updateStickyGroup()
+  }, [listItems, updateStickyGroup])
+
+  // 处理列表滚动事件，更新lastScrollOffset并更新sticky分组
+  const handleScroll = useCallback(
+    ({ scrollOffset }) => {
+      lastScrollOffsetRef.current = scrollOffset
+      updateStickyGroup(scrollOffset)
+    },
+    [updateStickyGroup]
+  )
+
  // 获取可选择的模型项（过滤掉分组标题）
  const modelItems = useMemo(() => {
    return listItems.filter((item) => item.type === 'model')
@@ -257,9 +298,6 @@ const PopupContainer: React.FC<PopupContainerProps> = ({ model, resolve }) => {
    const alignment = scrollTriggerRef.current === 'keyboard' ? 'auto' : 'center'
    listRef.current?.scrollToItem(index, alignment)

-    console.log('focusedItemKey', focusedItemKey)
-    console.log('scrollToFocusedItem', index, alignment)
-
    // 滚动后重置触发器
    scrollTriggerRef.current = 'none'
  }, [focusedItemKey, listItems])
@@ -365,41 +403,19 @@ const PopupContainer: React.FC<PopupContainerProps> = ({ model, resolve }) => {
    if (!open) return
    setTimeout(() => inputRef.current?.focus(), 0)
    scrollTriggerRef.current = 'initial'
+    lastScrollOffsetRef.current = 0
  }, [open])

-  // 初始化sticky分组标题
-  useEffect(() => {
-    if (firstGroupRef.current) {
-      setCurrentStickyGroup(firstGroupRef.current)
-    }
-  }, [listItems])
-
-  const handleItemsRendered = useCallback(
-    ({ visibleStartIndex }: { visibleStartIndex: number; visibleStopIndex: number }) => {
-      // 从可见区域的起始位置向前查找最近的分组标题
-      for (let i = visibleStartIndex - 1; i >= 0; i--) {
-        if (listItems[i]?.type === 'group') {
-          setCurrentStickyGroup(listItems[i])
-          return
-        }
-      }
-
-      // 找不到则使用第一个分组标题
-      setCurrentStickyGroup(firstGroupRef.current ?? null)
-    },
-    [listItems]
-  )
-
  const RowData = useMemo(
    (): VirtualizedRowData => ({
      listItems,
      focusedItemKey,
      setFocusedItemKey,
-      currentStickyGroup,
+      stickyGroup,
      handleItemClick,
      togglePin
    }),
-    [currentStickyGroup, focusedItemKey, handleItemClick, listItems, togglePin]
+    [stickyGroup, focusedItemKey, handleItemClick, listItems, togglePin]
  )

  const listHeight = useMemo(() => {
@@ -456,7 +472,7 @@ const PopupContainer: React.FC<PopupContainerProps> = ({ model, resolve }) => {
      {listItems.length > 0 ? (
        <ListContainer onMouseMove={() => setIsMouseOver(true)}>
          {/* Sticky Group Banner，它会替换第一个分组名称 */}
-          <StickyGroupBanner>{currentStickyGroup?.name}</StickyGroupBanner>
+          <StickyGroupBanner>{stickyGroup?.name}</StickyGroupBanner>
          <FixedSizeList
            ref={listRef}
            height={listHeight}
@@ -466,7 +482,7 @@ const PopupContainer: React.FC<PopupContainerProps> = ({ model, resolve }) => {
            itemData={RowData}
            itemKey={(index, data) => data.listItems[index].key}
            overscanCount={4}
-            onItemsRendered={handleItemsRendered}
+            onScroll={handleScroll}
            style={{ pointerEvents: isMouseOver ? 'auto' : 'none' }}>
            {VirtualizedRow}
          </FixedSizeList>
@@ -484,7 +500,7 @@ interface VirtualizedRowData {
  listItems: FlatListItem[]
  focusedItemKey: string
  setFocusedItemKey: (key: string) => void
-  currentStickyGroup: FlatListItem | null
+  stickyGroup: FlatListItem | null
  handleItemClick: (item: FlatListItem) => void
  togglePin: (modelId: string) => void
 }
@@ -494,7 +510,7 @@ interface VirtualizedRowData {
 */
 const VirtualizedRow = React.memo(
  ({ data, index, style }: { data: VirtualizedRowData; index: number; style: React.CSSProperties }) => {
-    const { listItems, focusedItemKey, setFocusedItemKey, handleItemClick, togglePin, currentStickyGroup } = data
+    const { listItems, focusedItemKey, setFocusedItemKey, handleItemClick, togglePin, stickyGroup } = data

    const item = listItems[index]

@@ -505,7 +521,7 @@ const VirtualizedRow = React.memo(
    return (
      <div style={style}>
        {item.type === 'group' ? (
-          <GroupItem $isSticky={item.key === currentStickyGroup?.key}>{item.name}</GroupItem>
+          <GroupItem $isSticky={item.key === stickyGroup?.key}>{item.name}</GroupItem>
        ) : (
          <ModelItem
            className={classNames({
--- a/src/renderer/src/config/minapps.ts
+++ b/src/renderer/src/config/minapps.ts
@@ -1,9 +1,7 @@
-import n8nLogo from '@renderer/assets/images/apps/n8n.ico?url'
-import ParateraLogo from '@renderer/assets/images/apps/paratera.ico?url'
-import ApplicationLogo from '@renderer/assets/images/apps/application.png?url'
 import ThreeMinTopAppLogo from '@renderer/assets/images/apps/3mintop.png?url'
 import AbacusLogo from '@renderer/assets/images/apps/abacus.webp?url'
 import AIStudioLogo from '@renderer/assets/images/apps/aistudio.svg?url'
+import ApplicationLogo from '@renderer/assets/images/apps/application.png?url'
 import BaiduAiAppLogo from '@renderer/assets/images/apps/baidu-ai.png?url'
 import BaiduAiSearchLogo from '@renderer/assets/images/apps/baidu-ai-search.webp?url'
 import BaicuanAppLogo from '@renderer/assets/images/apps/baixiaoying.webp?url'
@@ -29,6 +27,7 @@ import LambdaChatLogo from '@renderer/assets/images/apps/lambdachat.webp?url'
 import LeChatLogo from '@renderer/assets/images/apps/lechat.png?url'
 import MetasoAppLogo from '@renderer/assets/images/apps/metaso.webp?url'
 import MonicaLogo from '@renderer/assets/images/apps/monica.webp?url'
+import n8nLogo from '@renderer/assets/images/apps/n8n.svg?url'
 import NamiAiLogo from '@renderer/assets/images/apps/nm.png?url'
 import NamiAiSearchLogo from '@renderer/assets/images/apps/nm-search.webp?url'
 import NotebookLMAppLogo from '@renderer/assets/images/apps/notebooklm.svg?url'
@@ -62,11 +61,11 @@ const loadCustomMiniApp = async (): Promise<MinAppType[]> => {
  try {
    let content: string
    try {
-      content = await window.api.file.read('customMiniAPP')
+      content = await window.api.file.read('custom-minapps.json')
    } catch (error) {
      // 如果文件不存在，创建一个空的 JSON 数组
      content = '[]'
-      await window.api.file.writeWithId('customMiniAPP', content)
+      await window.api.file.writeWithId('custom-minapps.json', content)
    }

    const customApps = JSON.parse(content)
@@ -451,18 +450,15 @@ const ORIGIN_DEFAULT_MIN_APPS: MinAppType[] = [
      padding: 10
    }
  },
-  {
-    id: 'paratera',
-    name: 'ParateraAI',
-    logo: ParateraLogo,
-    url: 'https://ai.paratera.com/'
-  },
  {
    id: 'n8n',
    name: 'n8n',
    logo: n8nLogo,
    url: 'https://app.n8n.cloud/',
-    bodered: true
+    bodered: true,
+    style: {
+      padding: 5
+    }
  }
 ]

--- a/src/renderer/src/config/models.ts
+++ b/src/renderer/src/config/models.ts
@@ -2074,62 +2074,6 @@ export const SYSTEM_MODELS: Record<string, Model[]> = {
      name: 'Qwen2.5 72B Instruct',
      group: 'Qwen'
    }
-  ],
-  paratera: [
-    {
-      id: 'GLM-Z1-Flash-P002',
-      provider: 'paratera',
-      name: 'GLM-Z1-Flash-P002',
-      group: 'GLM'
-    },
-    {
-      id: 'GLM-Z1-AirX-P002',
-      provider: 'paratera',
-      name: 'GLM-Z1-AirX-P002',
-      group: 'GLM'
-    },
-    {
-      id: 'DeepSeek-V3-250324-P001',
-      provider: 'paratera',
-      name: 'DeepSeek-V3-250324-P001',
-      group: 'DeepSeek'
-    },
-    {
-      id: 'DeepSeek-R1',
-      provider: 'paratera',
-      name: 'DeepSeek-R1',
-      group: 'DeepSeek'
-    },
-    {
-      id: 'QwQ-N011-32B',
-      provider: 'paratera',
-      name: 'QwQ-N011-32B',
-      group: 'Qwen'
-    },
-    {
-      id: 'GLM-Embedding-2-P002',
-      provider: 'paratera',
-      name: 'GLM-Embedding-2-P002',
-      group: 'GLM'
-    },
-    {
-      id: 'GLM-Embedding-3-P002',
-      provider: 'paratera',
-      name: 'GLM-Embedding-3-P002',
-      group: 'GLM'
-    },
-    {
-      id: 'Doubao-Embedding-Text-P001',
-      provider: 'paratera',
-      name: 'Doubao-Embedding-Text-P001',
-      group: 'Doubao'
-    },
-    {
-      id: 'Doubao-Embedding-Large-Text-P001',
-      provider: 'paratera',
-      name: 'Doubao-Embedding-Large-Text-P001',
-      group: 'Doubao'
-    }
  ]
 }

--- a/src/renderer/src/config/providers.ts
+++ b/src/renderer/src/config/providers.ts
@@ -42,7 +42,6 @@ import VoyageAIProviderLogo from '@renderer/assets/images/providers/voyageai.png
 import XirangProviderLogo from '@renderer/assets/images/providers/xirang.png'
 import ZeroOneProviderLogo from '@renderer/assets/images/providers/zero-one.png'
 import ZhipuProviderLogo from '@renderer/assets/images/providers/zhipu.png'
-import ParateraLogo from '@renderer/assets/images/apps/paratera.ico'

 const PROVIDER_LOGO_MAP = {
  openai: OpenAiProviderLogo,
@@ -89,8 +88,7 @@ const PROVIDER_LOGO_MAP = {
  gpustack: GPUStackProviderLogo,
  alayanew: AlayaNewProviderLogo,
  voyageai: VoyageAIProviderLogo,
-  qiniu: QiniuProviderLogo,
-  paratera: ParateraLogo
+  qiniu: QiniuProviderLogo
 } as const

 export function getProviderLogo(providerId: string) {
@@ -585,16 +583,5 @@ export const PROVIDER_CONFIG = {
      docs: 'https://developer.qiniu.com/aitokenapi',
      models: 'https://developer.qiniu.com/aitokenapi/12883/model-list'
    }
-  },
-  paratera: {
-    api: {
-      url: 'https://llmapi.paratera.com'
-    },
-    websites: {
-      official: 'https://ai.paratera.com/',
-      apiKey: 'https://ai.paratera.com/#/lms/api',
-      docs: 'https://ai.paratera.com/document/llm/quickStart/useApi',
-      models: 'https://ai.paratera.com/#/lms/model'
-    }
  }
 }
--- a/src/renderer/src/i18n/locales/en-us.json
+++ b/src/renderer/src/i18n/locales/en-us.json
@@ -705,6 +705,7 @@
      "rerank_model_tooltip": "Click the Manage button in Settings -> Model Services to add.",
      "search": "Search models...",
      "stream_output": "Stream output",
+      "enable_tool_use": "Enable Tool Use",
      "type": {
        "embedding": "Embedding",
        "free": "Free",
--- a/src/renderer/src/i18n/locales/ja-jp.json
+++ b/src/renderer/src/i18n/locales/ja-jp.json
@@ -705,6 +705,7 @@
      "rerank_model_tooltip": "設定->モデルサービスに移動し、管理ボタンをクリックして追加します。",
      "search": "モデルを検索...",
      "stream_output": "ストリーム出力",
+      "enable_tool_use": "ツール呼び出し",
      "type": {
        "embedding": "埋め込み",
        "free": "無料",
--- a/src/renderer/src/i18n/locales/ru-ru.json
+++ b/src/renderer/src/i18n/locales/ru-ru.json
@@ -705,6 +705,7 @@
      "rerank_model_tooltip": "В настройках -> Служба модели нажмите кнопку \"Управление\", чтобы добавить.",
      "search": "Поиск моделей...",
      "stream_output": "Потоковый вывод",
+      "enable_tool_use": "Вызов инструмента",
      "type": {
        "embedding": "Встраиваемые",
        "free": "Бесплатные",
--- a/src/renderer/src/i18n/locales/zh-cn.json
+++ b/src/renderer/src/i18n/locales/zh-cn.json
@@ -705,6 +705,7 @@
      "rerank_model_tooltip": "在设置->模型服务中点击管理按钮添加",
      "search": "搜索模型...",
      "stream_output": "流式输出",
+      "enable_tool_use": "工具调用",
      "type": {
        "embedding": "嵌入",
        "free": "免费",
--- a/src/renderer/src/i18n/locales/zh-tw.json
+++ b/src/renderer/src/i18n/locales/zh-tw.json
@@ -705,6 +705,7 @@
      "rerank_model_tooltip": "在設定->模型服務中點擊管理按鈕添加",
      "search": "搜尋模型...",
      "stream_output": "串流輸出",
+      "enable_tool_use": "工具調用",
      "type": {
        "embedding": "嵌入",
        "free": "免費",
--- a/src/renderer/src/pages/apps/App.tsx
+++ b/src/renderer/src/pages/apps/App.tsx
@@ -40,7 +40,7 @@ const App: FC<Props> = ({ app, onClick, size = 60, isLast }) => {

  const handleAddCustomApp = async (values: any) => {
    try {
-      const content = await window.api.file.read('customMiniAPP')
+      const content = await window.api.file.read('custom-minapps.json')
      const customApps = JSON.parse(content)

      // Check for duplicate ID
@@ -62,7 +62,7 @@ const App: FC<Props> = ({ app, onClick, size = 60, isLast }) => {
        addTime: new Date().toISOString()
      }
      customApps.push(newApp)
-      await window.api.file.writeWithId('customMiniAPP', JSON.stringify(customApps, null, 2))
+      await window.api.file.writeWithId('custom-minapps.json', JSON.stringify(customApps, null, 2))
      message.success(t('settings.miniapps.custom.save_success'))
      setIsModalVisible(false)
      form.resetFields()
@@ -138,10 +138,10 @@ const App: FC<Props> = ({ app, onClick, size = 60, isLast }) => {
                danger: true,
                onClick: async () => {
                  try {
-                    const content = await window.api.file.read('customMiniAPP')
+                    const content = await window.api.file.read('custom-minapps.json')
                    const customApps = JSON.parse(content)
                    const updatedApps = customApps.filter((customApp: MinAppType) => customApp.id !== app.id)
-                    await window.api.file.writeWithId('customMiniAPP', JSON.stringify(updatedApps, null, 2))
+                    await window.api.file.writeWithId('custom-minapps.json', JSON.stringify(updatedApps, null, 2))
                    message.success(t('settings.miniapps.custom.remove_success'))
                    const reloadedApps = [...ORIGIN_DEFAULT_MIN_APPS, ...(await loadCustomMiniApp())]
                    updateDefaultMinApps(reloadedApps)
--- a/src/renderer/src/pages/home/Messages/CitationsList.tsx
+++ b/src/renderer/src/pages/home/Messages/CitationsList.tsx
@@ -91,7 +91,7 @@ const CitationsList: React.FC<CitationsListProps> = ({ citations }) => {
          onClose={() => setOpen(false)}
          open={open}
          width={680}
-          styles={{ header: { border: 'none' }, body: { paddingTop: 0, backgroundColor: 'var(--color-background)' } }}
+          styles={{ header: { border: 'none' }, body: { paddingTop: 0 } }}
          destroyOnClose={false}>
          {open &&
            citations.map((citation) => (
@@ -127,12 +127,12 @@ const WebSearchCitation: React.FC<{ citation: Citation }> = ({ citation }) => {
  })

  return (
-    <WebSearchCard onClick={() => handleLinkClick(citation.url)}>
+    <WebSearchCard>
      <WebSearchCardHeader>
        {citation.showFavicon && citation.url && (
          <Favicon hostname={new URL(citation.url).hostname} alt={citation.title || citation.hostname || ''} />
        )}
-        <CitationLink className="text-nowrap">
+        <CitationLink className="text-nowrap" href={citation.url} onClick={(e) => handleLinkClick(citation.url, e)}>
          {citation.title || <span className="hostname">{citation.hostname}</span>}
        </CitationLink>
      </WebSearchCardHeader>
@@ -146,10 +146,12 @@ const WebSearchCitation: React.FC<{ citation: Citation }> = ({ citation }) => {
 }

 const KnowledgeCitation: React.FC<{ citation: Citation }> = ({ citation }) => (
-  <WebSearchCard onClick={() => handleLinkClick(citation.url)}>
+  <WebSearchCard>
    <WebSearchCardHeader>
      {citation.showFavicon && <FileSearch width={16} />}
-      <CitationLink className="text-nowrap">{citation.title}</CitationLink>
+      <CitationLink className="text-nowrap" href={citation.url} onClick={(e) => handleLinkClick(citation.url, e)}>
+        {citation.title}
+      </CitationLink>
    </WebSearchCardHeader>
    <WebSearchCardContent>{citation.content && truncateText(citation.content, 100)}</WebSearchCardContent>
  </WebSearchCard>
@@ -189,11 +191,15 @@ const PreviewIcon = styled.div`
  }
 `

-const CitationLink = styled.div`
+const CitationLink = styled.a`
  font-size: 14px;
  line-height: 1.6;
  color: var(--color-text-1);
  text-decoration: none;
+
+  .hostname {
+    color: var(--color-link);
+  }
 `

 const WebSearchCard = styled.div`
@@ -204,11 +210,6 @@ const WebSearchCard = styled.div`
  border-radius: var(--list-item-border-radius);
  background-color: var(--color-background);
  transition: all 0.3s ease;
-  cursor: pointer;
-
-  &:hover {
-    background-color: var(--color-background-soft);
-  }
 `

 const WebSearchCardHeader = styled.div`
@@ -217,7 +218,6 @@ const WebSearchCardHeader = styled.div`
  align-items: center;
  gap: 8px;
  margin-bottom: 6px;
-  font-weight: 500;
 `

 const WebSearchCardContent = styled.div`
--- a/src/renderer/src/pages/home/Messages/MessageTools.tsx
+++ b/src/renderer/src/pages/home/Messages/MessageTools.tsx
@@ -67,7 +67,7 @@ const MessageTools: FC<Props> = ({ blocks }) => {
    const isDone = status === 'done'
    const hasError = isDone && response?.isError === true
    const result = {
-      params: tool.inputSchema,
+      params: toolResponse.arguments,
      response: toolResponse.response
    }

--- a/src/renderer/src/pages/home/Tabs/SettingsTab.tsx
+++ b/src/renderer/src/pages/home/Tabs/SettingsTab.tsx
@@ -70,6 +70,7 @@ const SettingsTab: FC<Props> = (props) => {
  const [maxTokens, setMaxTokens] = useState(assistant?.settings?.maxTokens ?? 0)
  const [fontSizeValue, setFontSizeValue] = useState(fontSize)
  const [streamOutput, setStreamOutput] = useState(assistant?.settings?.streamOutput ?? true)
+  const [enableToolUse, setEnableToolUse] = useState(assistant?.settings?.enableToolUse ?? false)
  const { t } = useTranslation()

  const dispatch = useAppDispatch()
@@ -222,6 +223,18 @@ const SettingsTab: FC<Props> = (props) => {
          />
        </SettingRow>
        <SettingDivider />
+        <SettingRow>
+          <SettingRowTitleSmall>{t('models.enable_tool_use')}</SettingRowTitleSmall>
+          <Switch
+            size="small"
+            checked={enableToolUse}
+            onChange={(checked) => {
+              setEnableToolUse(checked)
+              updateAssistantSettings({ enableToolUse: checked })
+            }}
+          />
+        </SettingRow>
+        <SettingDivider />
        <Row align="middle" justify="space-between" style={{ marginBottom: 10 }}>
          <HStack alignItems="center">
            <Label>{t('chat.settings.max_tokens')}</Label>
--- a/src/renderer/src/pages/paintings/AihubmixPage.tsx
+++ b/src/renderer/src/pages/paintings/AihubmixPage.tsx
@@ -1,4 +1,4 @@
-import { InfoCircleFilled, PlusOutlined, RedoOutlined } from '@ant-design/icons'
+import { PlusOutlined, RedoOutlined } from '@ant-design/icons'
 import IcImageUp from '@renderer/assets/images/paintings/ic_ImageUp.svg'
 import { Navbar, NavbarCenter, NavbarRight } from '@renderer/components/app/Navbar'
 import { HStack } from '@renderer/components/Layout'
@@ -20,6 +20,7 @@ import type { PaintingAction, PaintingsState } from '@renderer/types'
 import { getErrorMessage, uuid } from '@renderer/utils'
 import { Avatar, Button, Input, InputNumber, Radio, Segmented, Select, Slider, Switch, Tooltip, Upload } from 'antd'
 import TextArea from 'antd/es/input/TextArea'
+import { Info } from 'lucide-react'
 import type { FC } from 'react'
 import { useEffect, useMemo, useRef, useState } from 'react'
 import { useTranslation } from 'react-i18next'
@@ -72,6 +73,7 @@ const AihubmixPage: FC<{ Options: string[] }> = ({ Options }) => {
    { label: t('paintings.mode.remix'), value: 'remix' },
    { label: t('paintings.mode.upscale'), value: 'upscale' }
  ]
+
  const getNewPainting = () => {
    return {
      ...DEFAULT_PAINTING,
@@ -278,14 +280,6 @@ const AihubmixPage: FC<{ Options: string[] }> = ({ Options }) => {
    }

    removePainting(mode, paintingToDelete)
-
-    if (filteredPaintings.length === 1) {
-      const defaultPainting = {
-        ...DEFAULT_PAINTING,
-        id: uuid()
-      }
-      setPainting(defaultPainting)
-    }
  }

  const translate = async () => {
@@ -334,6 +328,7 @@ const AihubmixPage: FC<{ Options: string[] }> = ({ Options }) => {
      navigate('../' + providerId, { replace: true })
    }
  }
+
  // 处理模式切换
  const handleModeChange = (value: string) => {
    setMode(value as keyof PaintingsState)
@@ -494,8 +489,9 @@ const AihubmixPage: FC<{ Options: string[] }> = ({ Options }) => {

  useEffect(() => {
    if (filteredPaintings.length === 0) {
-      addPainting(mode, getNewPainting())
-      setPainting(DEFAULT_PAINTING)
+      const newPainting = getNewPainting()
+      addPainting(mode, newPainting)
+      setPainting(newPainting)
    }
  }, [filteredPaintings, mode, addPainting, painting])

@@ -674,11 +670,17 @@ const ToolbarMenu = styled.div`
  gap: 6px;
 `

-const InfoIcon = styled(InfoCircleFilled)`
+const InfoIcon = styled(Info)`
  margin-left: 5px;
  cursor: help;
-  color: #8d94a6;
-  font-size: 12px;
+  color: var(--color-text-2);
+  opacity: 0.6;
+  width: 14px;
+  height: 16px;
+
+  &:hover {
+    opacity: 1;
+  }
 `

 const SliderContainer = styled.div`
--- a/src/renderer/src/pages/paintings/PaintingsPage.tsx
+++ b/src/renderer/src/pages/paintings/PaintingsPage.tsx
@@ -1,4 +1,4 @@
-import { PlusOutlined, QuestionCircleOutlined, RedoOutlined } from '@ant-design/icons'
+import { PlusOutlined, RedoOutlined } from '@ant-design/icons'
 import ImageSize1_1 from '@renderer/assets/images/paintings/image-size-1-1.svg'
 import ImageSize1_2 from '@renderer/assets/images/paintings/image-size-1-2.svg'
 import ImageSize3_2 from '@renderer/assets/images/paintings/image-size-3-2.svg'
@@ -26,6 +26,7 @@ import type { FileType, Painting } from '@renderer/types'
 import { getErrorMessage, uuid } from '@renderer/utils'
 import { Button, Input, InputNumber, Radio, Select, Slider, Switch, Tooltip } from 'antd'
 import TextArea from 'antd/es/input/TextArea'
+import { Info } from 'lucide-react'
 import type { FC } from 'react'
 import { useEffect, useRef, useState } from 'react'
 import { useTranslation } from 'react-i18next'
@@ -90,7 +91,7 @@ const DEFAULT_PAINTING: Painting = {
 const PaintingsPage: FC<{ Options: string[] }> = ({ Options }) => {
  const { t } = useTranslation()
  const { paintings, addPainting, removePainting, updatePainting } = usePaintings()
-  const [painting, setPainting] = useState<Painting>(DEFAULT_PAINTING)
+  const [painting, setPainting] = useState<Painting>(paintings[0] || DEFAULT_PAINTING)
  const { theme } = useTheme()
  const providers = useAllProviders()
  const providerOptions = Options.map((option) => {
@@ -260,10 +261,6 @@ const PaintingsPage: FC<{ Options: string[] }> = ({ Options }) => {
    }

    removePainting('paintings', paintingToDelete)
-
-    if (paintings.length === 1) {
-      setPainting(getNewPainting())
-    }
  }

  const onSelectPainting = (newPainting: Painting) => {
@@ -326,8 +323,11 @@ const PaintingsPage: FC<{ Options: string[] }> = ({ Options }) => {

  useEffect(() => {
    if (paintings.length === 0) {
-      addPainting('paintings', getNewPainting())
+      const newPainting = getNewPainting()
+      addPainting('paintings', newPainting)
+      setPainting(newPainting)
    }
+
    return () => {
      if (spaceClickTimer.current) {
        clearTimeout(spaceClickTimer.current)
@@ -602,11 +602,13 @@ const RadioButton = styled(Radio.Button)`
  align-items: center;
 `

-const InfoIcon = styled(QuestionCircleOutlined)`
+const InfoIcon = styled(Info)`
  margin-left: 5px;
  cursor: help;
  color: var(--color-text-2);
  opacity: 0.6;
+  width: 16px;
+  height: 16px;

  &:hover {
    opacity: 1;
--- a/src/renderer/src/pages/settings/AssistantSettings/AssistantModelSettings.tsx
+++ b/src/renderer/src/pages/settings/AssistantSettings/AssistantModelSettings.tsx
@@ -24,6 +24,7 @@ const AssistantModelSettings: FC<Props> = ({ assistant, updateAssistant, updateA
  const [enableMaxTokens, setEnableMaxTokens] = useState(assistant?.settings?.enableMaxTokens ?? false)
  const [maxTokens, setMaxTokens] = useState(assistant?.settings?.maxTokens ?? 0)
  const [streamOutput, setStreamOutput] = useState(assistant?.settings?.streamOutput ?? true)
+  const [enableToolUse, setEnableToolUse] = useState(assistant?.settings?.enableToolUse ?? false)
  const [defaultModel, setDefaultModel] = useState(assistant?.defaultModel)
  const [topP, setTopP] = useState(assistant?.settings?.topP ?? 1)
  const [customParameters, setCustomParameters] = useState<AssistantSettingCustomParameters[]>(
@@ -377,6 +378,18 @@ const AssistantModelSettings: FC<Props> = ({ assistant, updateAssistant, updateA
        />
      </SettingRow>
      <Divider style={{ margin: '10px 0' }} />
+      <SettingRow style={{ minHeight: 30 }}>
+        <Label>{t('models.enable_tool_use')}</Label>
+        <Switch
+          size="small"
+          checked={enableToolUse}
+          onChange={(checked) => {
+            setEnableToolUse(checked)
+            updateAssistantSettings({ enableToolUse: checked })
+          }}
+        />
+      </SettingRow>
+      <Divider style={{ margin: '10px 0' }} />
      <SettingRow style={{ minHeight: 30 }}>
        <Label>{t('models.custom_parameters')}</Label>
        <Button icon={<PlusOutlined />} onClick={onAddCustomParameter}>
--- a/src/renderer/src/pages/settings/MiniappSettings/MiniAppSettings.tsx
+++ b/src/renderer/src/pages/settings/MiniappSettings/MiniAppSettings.tsx
@@ -1,10 +1,5 @@
 import { UndoOutlined } from '@ant-design/icons' // 导入重置图标
-import {
-  DEFAULT_MIN_APPS,
-  loadCustomMiniApp,
-  ORIGIN_DEFAULT_MIN_APPS,
-  updateDefaultMinApps
-} from '@renderer/config/minapps'
+import { DEFAULT_MIN_APPS } from '@renderer/config/minapps'
 import { useTheme } from '@renderer/context/ThemeProvider'
 import { useMinapps } from '@renderer/hooks/useMinapps'
 import { useSettings } from '@renderer/hooks/useSettings'
@@ -14,7 +9,7 @@ import {
  setMinappsOpenLinkExternal,
  setShowOpenedMinappsInSidebar
 } from '@renderer/store/settings'
-import { Button, Input, message, Slider, Switch, Tooltip } from 'antd'
+import { Button, message, Slider, Switch, Tooltip } from 'antd'
 import { FC, useCallback, useEffect, useRef, useState } from 'react'
 import { useTranslation } from 'react-i18next'
 import styled from 'styled-components'
@@ -36,92 +31,6 @@ const MiniAppSettings: FC = () => {
  const [disabledMiniApps, setDisabledMiniApps] = useState(disabled || [])
  const [messageApi, contextHolder] = message.useMessage()
  const debounceTimerRef = useRef<NodeJS.Timeout | null>(null)
-  const [customMiniAppContent, setCustomMiniAppContent] = useState('[]')
-
-  // 加载自定义小应用配置
-  useEffect(() => {
-    const loadCustomMiniApp = async () => {
-      try {
-        const content = await window.api.file.read('customMiniAPP')
-        let validContent = '[]'
-        try {
-          const parsed = JSON.parse(content)
-          validContent = JSON.stringify(parsed)
-        } catch (e) {
-          console.error('Invalid JSON format in custom mini app config:', e)
-        }
-        setCustomMiniAppContent(validContent)
-      } catch (error) {
-        console.error('Failed to load custom mini app config:', error)
-        setCustomMiniAppContent('[]')
-      }
-    }
-    loadCustomMiniApp()
-  }, [])
-
-  // 保存自定义小应用配置
-  const handleSaveCustomMiniApp = useCallback(async () => {
-    try {
-      // 验证 JSON 格式
-      if (customMiniAppContent === '') {
-        setCustomMiniAppContent('[]')
-      }
-      const parsedContent = JSON.parse(customMiniAppContent)
-      // 确保是数组
-      if (!Array.isArray(parsedContent)) {
-        throw new Error('Content must be an array')
-      }
-
-      // 检查自定义应用中的重复ID
-      const customIds = new Set<string>()
-      const duplicateIds = new Set<string>()
-      parsedContent.forEach((app: any) => {
-        if (app.id) {
-          if (customIds.has(app.id)) {
-            duplicateIds.add(app.id)
-          }
-          customIds.add(app.id)
-        }
-      })
-
-      // 检查与默认应用的ID重复
-      const defaultIds = new Set(ORIGIN_DEFAULT_MIN_APPS.map((app) => app.id))
-      const conflictingIds = new Set<string>()
-      customIds.forEach((id) => {
-        if (defaultIds.has(id)) {
-          conflictingIds.add(id)
-        }
-      })
-
-      // 如果有重复ID，显示错误信息
-      if (duplicateIds.size > 0 || conflictingIds.size > 0) {
-        let errorMessage = ''
-        if (duplicateIds.size > 0) {
-          errorMessage += t('settings.miniapps.custom.duplicate_ids', { ids: Array.from(duplicateIds).join(', ') })
-        }
-        if (conflictingIds.size > 0) {
-          console.log('conflictingIds', Array.from(conflictingIds))
-          if (errorMessage) errorMessage += '\n'
-          errorMessage += t('settings.miniapps.custom.conflicting_ids', { ids: Array.from(conflictingIds).join(', ') })
-        }
-        messageApi.error(errorMessage)
-        return
-      }
-
-      // 保存文件
-      await window.api.file.writeWithId('customMiniAPP', customMiniAppContent)
-      messageApi.success(t('settings.miniapps.custom.save_success'))
-      // 重新加载应用列表
-      console.log('Reloading mini app list...')
-      const reloadedApps = [...ORIGIN_DEFAULT_MIN_APPS, ...(await loadCustomMiniApp())]
-      updateDefaultMinApps(reloadedApps)
-      console.log('Reloaded mini app list:', reloadedApps)
-      updateMinapps(reloadedApps)
-    } catch (error) {
-      messageApi.error(t('settings.miniapps.custom.save_error'))
-      console.error('Failed to save custom mini app config:', error)
-    }
-  }, [customMiniAppContent, messageApi, t, updateMinapps])

  const handleResetMinApps = useCallback(() => {
    setVisibleMiniApps(DEFAULT_MIN_APPS)
@@ -235,30 +144,6 @@ const MiniAppSettings: FC = () => {
            onChange={(checked) => dispatch(setShowOpenedMinappsInSidebar(checked))}
          />
        </SettingRow>
-        <SettingDivider />
-        <SettingRow>
-          <SettingLabelGroup>
-            <SettingRowTitle>{t('settings.miniapps.custom.edit_title')}</SettingRowTitle>
-            <SettingDescription>{t('settings.miniapps.custom.edit_description')}</SettingDescription>
-          </SettingLabelGroup>
-        </SettingRow>
-        <CustomEditorContainer>
-          <Input.TextArea
-            value={customMiniAppContent}
-            onChange={(e) => setCustomMiniAppContent(e.target.value)}
-            placeholder={t('settings.miniapps.custom.placeholder')}
-            style={{
-              minHeight: 200,
-              fontFamily: 'monospace',
-              backgroundColor: 'var(--color-bg-2)',
-              color: 'var(--color-text)',
-              borderColor: 'var(--color-border)'
-            }}
-          />
-          <Button type="primary" onClick={handleSaveCustomMiniApp} style={{ marginTop: 8 }}>
-            {t('settings.miniapps.custom.save')}
-          </Button>
-        </CustomEditorContainer>
      </SettingGroup>
    </SettingContainer>
  )
--- a/src/renderer/src/providers/AiProvider/AihubmixProvider.ts
+++ b/src/renderer/src/providers/AiProvider/AihubmixProvider.ts
@@ -1,6 +1,6 @@
 import { isOpenAILLMModel } from '@renderer/config/models'
 import { getDefaultModel } from '@renderer/services/AssistantService'
-import { Assistant, Model, Provider, Suggestion } from '@renderer/types'
+import { Assistant, MCPCallToolResponse, MCPTool, MCPToolResponse, Model, Provider, Suggestion } from '@renderer/types'
 import { Message } from '@renderer/types/newMessage'
 import OpenAI from 'openai'

@@ -18,6 +18,7 @@ import OpenAIProvider from './OpenAIProvider'
 export default class AihubmixProvider extends BaseProvider {
  private providers: Map<string, BaseProvider> = new Map()
  private defaultProvider: BaseProvider
+  private currentProvider: BaseProvider

  constructor(provider: Provider) {
    super(provider)
@@ -30,6 +31,7 @@ export default class AihubmixProvider extends BaseProvider {

    // 设置默认提供商
    this.defaultProvider = this.providers.get('default')!
+    this.currentProvider = this.defaultProvider
  }

  /**
@@ -70,7 +72,8 @@ export default class AihubmixProvider extends BaseProvider {

  public async completions(params: CompletionsParams): Promise<void> {
    const model = params.assistant.model
-    return this.getProvider(model!).completions(params)
+    this.currentProvider = this.getProvider(model!)
+    return this.currentProvider.completions(params)
  }

  public async translate(
@@ -100,4 +103,12 @@ export default class AihubmixProvider extends BaseProvider {
  public async getEmbeddingDimensions(model: Model): Promise<number> {
    return this.getProvider(model).getEmbeddingDimensions(model)
  }
+
+  public convertMcpTools<T>(mcpTools: MCPTool[]) {
+    return this.currentProvider.convertMcpTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage(mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) {
+    return this.currentProvider.mcpToolCallResponseToMessage(mcpToolResponse, resp, model)
+  }
 }
--- a/src/renderer/src/providers/AiProvider/AnthropicProvider.ts
+++ b/src/renderer/src/providers/AiProvider/AnthropicProvider.ts
@@ -1,15 +1,19 @@
 import Anthropic from '@anthropic-ai/sdk'
 import {
+  Base64ImageSource,
+  ImageBlockParam,
  MessageCreateParamsNonStreaming,
  MessageParam,
  TextBlockParam,
+  ToolResultBlockParam,
  ToolUnion,
+  ToolUseBlock,
  WebSearchResultBlock,
  WebSearchTool20250305,
  WebSearchToolResultError
 } from '@anthropic-ai/sdk/resources'
 import { DEFAULT_MAX_TOKENS } from '@renderer/config/constant'
-import { isReasoningModel, isVisionModel, isWebSearchModel } from '@renderer/config/models'
+import { isReasoningModel, isWebSearchModel } from '@renderer/config/models'
 import { getStoreSetting } from '@renderer/hooks/useSettings'
 import i18n from '@renderer/i18n'
 import { getAssistantSettings, getDefaultModel, getTopNamingModel } from '@renderer/services/AssistantService'
@@ -23,16 +27,24 @@ import {
  Assistant,
  EFFORT_RATIO,
  FileTypes,
+  MCPCallToolResponse,
+  MCPTool,
  MCPToolResponse,
  Model,
  Provider,
  Suggestion,
+  ToolCallResponse,
  WebSearchSource
 } from '@renderer/types'
 import { ChunkType } from '@renderer/types/chunk'
 import type { Message } from '@renderer/types/newMessage'
 import { removeSpecialCharactersForTopicName } from '@renderer/utils'
-import { mcpToolCallResponseToAnthropicMessage, parseAndCallTools } from '@renderer/utils/mcp-tools'
+import {
+  anthropicToolUseToMcpTool,
+  mcpToolCallResponseToAnthropicMessage,
+  mcpToolsToAnthropicTools,
+  parseAndCallTools
+} from '@renderer/utils/mcp-tools'
 import { findFileBlocks, findImageBlocks, getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { buildSystemPrompt } from '@renderer/utils/prompt'
 import { first, flatten, sum, takeRight } from 'lodash'
@@ -199,7 +211,7 @@ export default class AnthropicProvider extends BaseProvider {
  public async completions({ messages, assistant, mcpTools, onChunk, onFilterMessages }: CompletionsParams) {
    const defaultModel = getDefaultModel()
    const model = assistant.model || defaultModel
-    const { contextCount, maxTokens, streamOutput } = getAssistantSettings(assistant)
+    const { contextCount, maxTokens, streamOutput, enableToolUse } = getAssistantSettings(assistant)

    const userMessagesParams: MessageParam[] = []

@@ -215,10 +227,16 @@ export default class AnthropicProvider extends BaseProvider {

    const userMessages = flatten(userMessagesParams)
    const lastUserMessage = _messages.findLast((m) => m.role === 'user')
-    // const tools = mcpTools ? mcpToolsToAnthropicTools(mcpTools) : undefined

    let systemPrompt = assistant.prompt
-    if (mcpTools && mcpTools.length > 0) {
+
+    const { tools } = this.setupToolsConfig<ToolUnion>({
+      model,
+      mcpTools,
+      enableToolUse
+    })
+
+    if (this.useSystemPromptForTools && mcpTools && mcpTools.length) {
      systemPrompt = buildSystemPrompt(systemPrompt, mcpTools)
    }

@@ -232,8 +250,6 @@ export default class AnthropicProvider extends BaseProvider {

    const isEnabledBuiltinWebSearch = assistant.enableWebSearch

-    const tools: ToolUnion[] = []
-
    if (isEnabledBuiltinWebSearch) {
      const webSearchTool = await this.getWebSearchParams(model)
      if (webSearchTool) {
@@ -244,7 +260,6 @@ export default class AnthropicProvider extends BaseProvider {
    const body: MessageCreateParamsNonStreaming = {
      model: model.id,
      messages: userMessages,
-      // tools: isEmpty(tools) ? undefined : tools,
      max_tokens: maxTokens || DEFAULT_MAX_TOKENS,
      temperature: this.getTemperature(assistant, model),
      top_p: this.getTopP(assistant, model),
@@ -303,7 +318,7 @@ export default class AnthropicProvider extends BaseProvider {
    const processStream = (body: MessageCreateParamsNonStreaming, idx: number) => {
      return new Promise<void>((resolve, reject) => {
        // 等待接口返回流
-        onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+        const toolCalls: ToolUseBlock[] = []
        let hasThinkingContent = false
        this.sdk.messages
          .stream({ ...body, stream: true }, { signal, timeout: 5 * 60 * 1000 })
@@ -380,30 +395,70 @@ export default class AnthropicProvider extends BaseProvider {
            })
            thinking_content += thinking
          })
+          .on('contentBlock', (content) => {
+            if (content.type === 'tool_use') {
+              toolCalls.push(content)
+            }
+          })
          .on('finalMessage', async (message) => {
+            const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+            // tool call
+            if (toolCalls.length > 0) {
+              const mcpToolResponses = toolCalls
+                .map((toolCall) => {
+                  const mcpTool = anthropicToolUseToMcpTool(mcpTools, toolCall)
+                  if (!mcpTool) {
+                    return undefined
+                  }
+                  return {
+                    id: toolCall.id,
+                    toolCallId: toolCall.id,
+                    tool: mcpTool,
+                    arguments: toolCall.input as Record<string, unknown>,
+                    status: 'pending'
+                  } as ToolCallResponse
+                })
+                .filter((t) => typeof t !== 'undefined')
+              toolResults.push(
+                ...(await parseAndCallTools(
+                  mcpToolResponses,
+                  toolResponses,
+                  onChunk,
+                  this.mcpToolCallResponseToMessage,
+                  model,
+                  mcpTools
+                ))
+              )
+            }
+
+            // tool use
            const content = message.content[0]
            if (content && content.type === 'text') {
              onChunk({ type: ChunkType.TEXT_COMPLETE, text: content.text })
-              const toolResults = await parseAndCallTools(
-                content.text,
-                toolResponses,
-                onChunk,
-                idx,
-                mcpToolCallResponseToAnthropicMessage,
-                mcpTools,
-                isVisionModel(model)
+              toolResults.push(
+                ...(await parseAndCallTools(
+                  content.text,
+                  toolResponses,
+                  onChunk,
+                  this.mcpToolCallResponseToMessage,
+                  model,
+                  mcpTools
+                ))
              )
-              if (toolResults.length > 0) {
-                userMessages.push({
-                  role: message.role,
-                  content: message.content
-                })
+            }

-                toolResults.forEach((ts) => userMessages.push(ts as MessageParam))
-                const newBody = body
-                newBody.messages = userMessages
-                await processStream(newBody, idx + 1)
-              }
+            userMessages.push({
+              role: message.role,
+              content: message.content
+            })
+
+            if (toolResults.length > 0) {
+              toolResults.forEach((ts) => userMessages.push(ts as MessageParam))
+              const newBody = body
+              newBody.messages = userMessages
+
+              onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+              await processStream(newBody, idx + 1)
            }

            const time_completion_millsec = new Date().getTime() - start_time_millsec
@@ -434,7 +489,7 @@ export default class AnthropicProvider extends BaseProvider {
          })
      })
    }
-
+    onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
    await processStream(body, 0).finally(cleanup)
  }

@@ -683,4 +738,47 @@ export default class AnthropicProvider extends BaseProvider {
  public async getEmbeddingDimensions(): Promise<number> {
    return 0
  }
+
+  public convertMcpTools<T>(mcpTools: MCPTool[]): T[] {
+    return mcpToolsToAnthropicTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage = (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => {
+    if ('toolUseId' in mcpToolResponse && mcpToolResponse.toolUseId) {
+      return mcpToolCallResponseToAnthropicMessage(mcpToolResponse, resp, model)
+    } else if ('toolCallId' in mcpToolResponse) {
+      return {
+        role: 'user',
+        content: [
+          {
+            type: 'tool_result',
+            tool_use_id: mcpToolResponse.toolCallId!,
+            content: resp.content
+              .map((item) => {
+                if (item.type === 'text') {
+                  return {
+                    type: 'text',
+                    text: item.text || ''
+                  } satisfies TextBlockParam
+                }
+                if (item.type === 'image') {
+                  return {
+                    type: 'image',
+                    source: {
+                      data: item.data || '',
+                      media_type: (item.mimeType || 'image/png') as Base64ImageSource['media_type'],
+                      type: 'base64'
+                    }
+                  } satisfies ImageBlockParam
+                }
+                return
+              })
+              .filter((n) => typeof n !== 'undefined'),
+            is_error: resp.isError
+          } satisfies ToolResultBlockParam
+        ]
+      }
+    }
+    return
+  }
 }
--- a/src/renderer/src/providers/AiProvider/BaseProvider.ts
+++ b/src/renderer/src/providers/AiProvider/BaseProvider.ts
@@ -1,9 +1,13 @@
+import { isFunctionCallingModel } from '@renderer/config/models'
 import { REFERENCE_PROMPT } from '@renderer/config/prompts'
 import { getLMStudioKeepAliveTime } from '@renderer/hooks/useLMStudio'
 import type {
  Assistant,
  GenerateImageParams,
  KnowledgeReference,
+  MCPCallToolResponse,
+  MCPTool,
+  MCPToolResponse,
  Model,
  Provider,
  Suggestion,
@@ -22,10 +26,15 @@ import type OpenAI from 'openai'
 import type { CompletionsParams } from '.'

 export default abstract class BaseProvider {
+  // Threshold for determining whether to use system prompt for tools
+  private static readonly SYSTEM_PROMPT_THRESHOLD: number = 128
+
  protected provider: Provider
  protected host: string
  protected apiKey: string

+  protected useSystemPromptForTools: boolean = true
+
  constructor(provider: Provider) {
    this.provider = provider
    this.host = this.getBaseURL()
@@ -47,6 +56,12 @@ export default abstract class BaseProvider {
  abstract generateImage(params: GenerateImageParams): Promise<string[]>
  abstract generateImageByChat({ messages, assistant, onChunk, onFilterMessages }: CompletionsParams): Promise<void>
  abstract getEmbeddingDimensions(model: Model): Promise<number>
+  public abstract convertMcpTools<T>(mcpTools: MCPTool[]): T[]
+  public abstract mcpToolCallResponseToMessage(
+    mcpToolResponse: MCPToolResponse,
+    resp: MCPCallToolResponse,
+    model: Model
+  ): any

  public getBaseURL(): string {
    const host = this.provider.apiHost
@@ -229,4 +244,31 @@ export default abstract class BaseProvider {
      cleanup
    }
  }
+
+  // Setup tools configuration based on provided parameters
+  protected setupToolsConfig<T>(params: { mcpTools?: MCPTool[]; model: Model; enableToolUse?: boolean }): {
+    tools: T[]
+  } {
+    const { mcpTools, model, enableToolUse } = params
+    let tools: T[] = []
+
+    // If there are no tools, return an empty array
+    if (!mcpTools?.length) {
+      return { tools }
+    }
+
+    // If the number of tools exceeds the threshold, use the system prompt
+    if (mcpTools.length > BaseProvider.SYSTEM_PROMPT_THRESHOLD) {
+      this.useSystemPromptForTools = true
+      return { tools }
+    }
+
+    // If the model supports function calling and tool usage is enabled
+    if (isFunctionCallingModel(model) && enableToolUse) {
+      tools = this.convertMcpTools<T>(mcpTools)
+      this.useSystemPromptForTools = false
+    }
+
+    return { tools }
+  }
 }
--- a/src/renderer/src/providers/AiProvider/GeminiProvider.ts
+++ b/src/renderer/src/providers/AiProvider/GeminiProvider.ts
@@ -1,6 +1,7 @@
 import {
  Content,
  File,
+  FunctionCall,
  GenerateContentConfig,
  GenerateContentResponse,
  GoogleGenAI,
@@ -11,8 +12,9 @@ import {
  PartUnion,
  SafetySetting,
  ThinkingConfig,
-  ToolListUnion
+  Tool
 } from '@google/genai'
+import { nanoid } from '@reduxjs/toolkit'
 import {
  findTokenLimit,
  isGeminiReasoningModel,
@@ -35,17 +37,25 @@ import {
  EFFORT_RATIO,
  FileType,
  FileTypes,
+  MCPCallToolResponse,
+  MCPTool,
  MCPToolResponse,
  Model,
  Provider,
  Suggestion,
+  ToolCallResponse,
  Usage,
  WebSearchSource
 } from '@renderer/types'
 import { BlockCompleteChunk, Chunk, ChunkType, LLMWebSearchCompleteChunk } from '@renderer/types/chunk'
 import type { Message, Response } from '@renderer/types/newMessage'
 import { removeSpecialCharactersForTopicName } from '@renderer/utils'
-import { mcpToolCallResponseToGeminiMessage, parseAndCallTools } from '@renderer/utils/mcp-tools'
+import {
+  geminiFunctionCallToMcpTool,
+  mcpToolCallResponseToGeminiMessage,
+  mcpToolsToGeminiTools,
+  parseAndCallTools
+} from '@renderer/utils/mcp-tools'
 import { findFileBlocks, findImageBlocks, getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { buildSystemPrompt } from '@renderer/utils/prompt'
 import { MB } from '@shared/config/constant'
@@ -263,7 +273,7 @@ export default class GeminiProvider extends BaseProvider {
  }: CompletionsParams): Promise<void> {
    const defaultModel = getDefaultModel()
    const model = assistant.model || defaultModel
-    const { contextCount, maxTokens, streamOutput } = getAssistantSettings(assistant)
+    const { contextCount, maxTokens, streamOutput, enableToolUse } = getAssistantSettings(assistant)

    const userMessages = filterUserRoleStartMessages(
      filterEmptyMessages(filterContextMessages(takeRight(messages, contextCount + 2)))
@@ -280,12 +290,16 @@ export default class GeminiProvider extends BaseProvider {

    let systemInstruction = assistant.prompt

-    if (mcpTools && mcpTools.length > 0) {
+    const { tools } = this.setupToolsConfig<Tool>({
+      mcpTools,
+      model,
+      enableToolUse
+    })
+
+    if (this.useSystemPromptForTools) {
      systemInstruction = buildSystemPrompt(assistant.prompt || '', mcpTools)
    }

-    // const tools = mcpToolsToGeminiTools(mcpTools)
-    const tools: ToolListUnion = []
    const toolResponses: MCPToolResponse[] = []

    if (assistant.enableWebSearch && isWebSearchModel(model)) {
@@ -351,6 +365,224 @@ export default class GeminiProvider extends BaseProvider {

    const { cleanup, abortController } = this.createAbortController(userLastMessage?.id, true)

+    const processToolResults = async (toolResults: Awaited<ReturnType<typeof parseAndCallTools>>, idx: number) => {
+      if (toolResults.length === 0) return
+      const newChat = this.sdk.chats.create({
+        model: model.id,
+        config: generateContentConfig,
+        history: history as Content[]
+      })
+
+      const newStream = await newChat.sendMessageStream({
+        message: flatten(toolResults.map((ts) => (ts as Content).parts)) as PartUnion,
+        config: {
+          ...generateContentConfig,
+          abortSignal: abortController.signal
+        }
+      })
+      await processStream(newStream, idx + 1)
+    }
+
+    const processToolCalls = async (toolCalls: FunctionCall[]) => {
+      const mcpToolResponses: ToolCallResponse[] = toolCalls
+        .map((toolCall) => {
+          const mcpTool = geminiFunctionCallToMcpTool(mcpTools, toolCall)
+          if (!mcpTool) return undefined
+
+          const parsedArgs = (() => {
+            try {
+              return typeof toolCall.args === 'string' ? JSON.parse(toolCall.args) : toolCall.args
+            } catch {
+              return toolCall.args
+            }
+          })()
+
+          return {
+            id: toolCall.id || nanoid(),
+            toolCallId: toolCall.id,
+            tool: mcpTool,
+            arguments: parsedArgs,
+            status: 'pending'
+          } as ToolCallResponse
+        })
+        .filter((t): t is ToolCallResponse => typeof t !== 'undefined')
+
+      return await parseAndCallTools(
+        mcpToolResponses,
+        toolResponses,
+        onChunk,
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
+      )
+    }
+
+    const processToolUses = async (content: string) => {
+      return await parseAndCallTools(
+        content,
+        toolResponses,
+        onChunk,
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
+      )
+    }
+
+    const processStream = async (
+      stream: AsyncGenerator<GenerateContentResponse> | GenerateContentResponse,
+      idx: number
+    ) => {
+      history.push(messageContents)
+
+      let functionCalls: FunctionCall[] = []
+
+      if (stream instanceof GenerateContentResponse) {
+        let content = ''
+        const time_completion_millsec = new Date().getTime() - start_time_millsec
+
+        const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+        if (stream.text?.length) {
+          toolResults.push(...(await processToolUses(stream.text)))
+        }
+        stream.candidates?.forEach((candidate) => {
+          if (candidate.content) {
+            history.push(candidate.content)
+
+            candidate.content.parts?.forEach((part) => {
+              if (part.functionCall) {
+                functionCalls.push(part.functionCall)
+              }
+              if (part.text) {
+                content += part.text
+                onChunk({ type: ChunkType.TEXT_DELTA, text: part.text })
+              }
+            })
+          }
+        })
+        if (content.length) {
+          onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
+        }
+        if (functionCalls.length) {
+          toolResults.push(...(await processToolCalls(functionCalls)))
+        }
+        if (stream.text?.length) {
+          toolResults.push(...(await processToolUses(stream.text)))
+        }
+        if (toolResults.length) {
+          await processToolResults(toolResults, idx)
+        }
+        onChunk({
+          type: ChunkType.BLOCK_COMPLETE,
+          response: {
+            text: stream.text,
+            usage: {
+              prompt_tokens: stream.usageMetadata?.promptTokenCount || 0,
+              thoughts_tokens: stream.usageMetadata?.thoughtsTokenCount || 0,
+              completion_tokens: stream.usageMetadata?.candidatesTokenCount || 0,
+              total_tokens: stream.usageMetadata?.totalTokenCount || 0
+            },
+            metrics: {
+              completion_tokens: stream.usageMetadata?.candidatesTokenCount,
+              time_completion_millsec,
+              time_first_token_millsec: 0
+            },
+            webSearch: {
+              results: stream.candidates?.[0]?.groundingMetadata,
+              source: 'gemini'
+            }
+          } as Response
+        } as BlockCompleteChunk)
+      } else {
+        let content = ''
+        let final_time_completion_millsec = 0
+        let lastUsage: Usage | undefined = undefined
+        for await (const chunk of stream) {
+          if (window.keyv.get(EVENT_NAMES.CHAT_COMPLETION_PAUSED)) break
+
+          // --- Calculate Metrics ---
+          if (time_first_token_millsec == 0 && chunk.text !== undefined) {
+            // Update based on text arrival
+            time_first_token_millsec = new Date().getTime() - start_time_millsec
+          }
+
+          // 1. Text Content
+          if (chunk.text !== undefined) {
+            content += chunk.text
+            onChunk({ type: ChunkType.TEXT_DELTA, text: chunk.text })
+          }
+
+          // 2. Usage Data
+          if (chunk.usageMetadata) {
+            lastUsage = {
+              prompt_tokens: chunk.usageMetadata.promptTokenCount || 0,
+              completion_tokens: chunk.usageMetadata.candidatesTokenCount || 0,
+              total_tokens: chunk.usageMetadata.totalTokenCount || 0
+            }
+            final_time_completion_millsec = new Date().getTime() - start_time_millsec
+          }
+
+          // 4. Image Generation
+          const generateImage = this.processGeminiImageResponse(chunk, onChunk)
+          if (generateImage?.images?.length) {
+            onChunk({ type: ChunkType.IMAGE_COMPLETE, image: generateImage })
+          }
+
+          if (chunk.candidates?.[0]?.finishReason) {
+            if (chunk.text) {
+              onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
+            }
+            if (chunk.candidates?.[0]?.groundingMetadata) {
+              // 3. Grounding/Search Metadata
+              const groundingMetadata = chunk.candidates?.[0]?.groundingMetadata
+              onChunk({
+                type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
+                llm_web_search: {
+                  results: groundingMetadata,
+                  source: WebSearchSource.GEMINI
+                }
+              } as LLMWebSearchCompleteChunk)
+            }
+            if (chunk.functionCalls) {
+              chunk.candidates?.forEach((candidate) => {
+                if (candidate.content) {
+                  history.push(candidate.content)
+                }
+              })
+              functionCalls = functionCalls.concat(chunk.functionCalls)
+            }
+
+            onChunk({
+              type: ChunkType.BLOCK_COMPLETE,
+              response: {
+                metrics: {
+                  completion_tokens: lastUsage?.completion_tokens,
+                  time_completion_millsec: final_time_completion_millsec,
+                  time_first_token_millsec
+                },
+                usage: lastUsage
+              }
+            })
+          }
+
+          // --- End Incremental onChunk calls ---
+
+          // Call processToolUses AFTER potentially processing text content in this chunk
+          // This assumes tools might be specified within the text stream
+          // Note: parseAndCallTools inside should handle its own onChunk for tool responses
+          let toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+          if (functionCalls.length) {
+            toolResults = await processToolCalls(functionCalls)
+          }
+          if (content.length) {
+            toolResults = toolResults.concat(await processToolUses(content))
+          }
+          if (toolResults.length) {
+            await processToolResults(toolResults, idx)
+          }
+        }
+      }
+    }
+
    if (!streamOutput) {
      const response = await chat.sendMessage({
        message: messageContents as PartUnion,
@@ -359,32 +591,10 @@ export default class GeminiProvider extends BaseProvider {
          abortSignal: abortController.signal
        }
      })
-      const time_completion_millsec = new Date().getTime() - start_time_millsec
-      onChunk({
-        type: ChunkType.BLOCK_COMPLETE,
-        response: {
-          text: response.text,
-          usage: {
-            prompt_tokens: response.usageMetadata?.promptTokenCount || 0,
-            thoughts_tokens: response.usageMetadata?.thoughtsTokenCount || 0,
-            completion_tokens: response.usageMetadata?.candidatesTokenCount || 0,
-            total_tokens: response.usageMetadata?.totalTokenCount || 0
-          },
-          metrics: {
-            completion_tokens: response.usageMetadata?.candidatesTokenCount,
-            time_completion_millsec,
-            time_first_token_millsec: 0
-          },
-          webSearch: {
-            results: response.candidates?.[0]?.groundingMetadata,
-            source: 'gemini'
-          }
-        } as Response
-      } as BlockCompleteChunk)
-      return
+      onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+      return await processStream(response, 0).then(cleanup)
    }

-    // 等待接口返回流
    onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
    const userMessagesStream = await chat.sendMessageStream({
      message: messageContents as PartUnion,
@@ -394,105 +604,6 @@ export default class GeminiProvider extends BaseProvider {
      }
    })

-    const processToolUses = async (content: string, idx: number) => {
-      const toolResults = await parseAndCallTools(
-        content,
-        toolResponses,
-        onChunk,
-        idx,
-        mcpToolCallResponseToGeminiMessage,
-        mcpTools,
-        isVisionModel(model)
-      )
-      if (toolResults && toolResults.length > 0) {
-        history.push(messageContents)
-        const newChat = this.sdk.chats.create({
-          model: model.id,
-          config: generateContentConfig,
-          history: history as Content[]
-        })
-        const newStream = await newChat.sendMessageStream({
-          message: flatten(toolResults.map((ts) => (ts as Content).parts)) as PartUnion,
-          config: {
-            ...generateContentConfig,
-            abortSignal: abortController.signal
-          }
-        })
-        await processStream(newStream, idx + 1)
-      }
-    }
-
-    const processStream = async (stream: AsyncGenerator<GenerateContentResponse>, idx: number) => {
-      let content = ''
-      let final_time_completion_millsec = 0
-      let lastUsage: Usage | undefined = undefined
-      for await (const chunk of stream) {
-        if (window.keyv.get(EVENT_NAMES.CHAT_COMPLETION_PAUSED)) break
-
-        // --- Calculate Metrics ---
-        if (time_first_token_millsec == 0 && chunk.text !== undefined) {
-          // Update based on text arrival
-          time_first_token_millsec = new Date().getTime() - start_time_millsec
-        }
-
-        // 1. Text Content
-        if (chunk.text !== undefined) {
-          content += chunk.text
-          onChunk({ type: ChunkType.TEXT_DELTA, text: chunk.text })
-        }
-
-        // 2. Usage Data
-        if (chunk.usageMetadata) {
-          lastUsage = {
-            prompt_tokens: chunk.usageMetadata.promptTokenCount || 0,
-            completion_tokens: chunk.usageMetadata.candidatesTokenCount || 0,
-            total_tokens: chunk.usageMetadata.totalTokenCount || 0
-          }
-          final_time_completion_millsec = new Date().getTime() - start_time_millsec
-        }
-
-        // 4. Image Generation
-        const generateImage = this.processGeminiImageResponse(chunk, onChunk)
-        if (generateImage?.images?.length) {
-          onChunk({ type: ChunkType.IMAGE_COMPLETE, image: generateImage })
-        }
-
-        if (chunk.candidates?.[0]?.finishReason) {
-          if (chunk.text) {
-            onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
-          }
-          if (chunk.candidates?.[0]?.groundingMetadata) {
-            // 3. Grounding/Search Metadata
-            const groundingMetadata = chunk.candidates?.[0]?.groundingMetadata
-            onChunk({
-              type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
-              llm_web_search: {
-                results: groundingMetadata,
-                source: WebSearchSource.GEMINI
-              }
-            } as LLMWebSearchCompleteChunk)
-          }
-          onChunk({
-            type: ChunkType.BLOCK_COMPLETE,
-            response: {
-              metrics: {
-                completion_tokens: lastUsage?.completion_tokens,
-                time_completion_millsec: final_time_completion_millsec,
-                time_first_token_millsec
-              },
-              usage: lastUsage
-            }
-          })
-        }
-        // --- End Incremental onChunk calls ---
-
-        // Call processToolUses AFTER potentially processing text content in this chunk
-        // This assumes tools might be specified within the text stream
-        // Note: parseAndCallTools inside should handle its own onChunk for tool responses
-        await processToolUses(content, idx)
-      }
-    }
-
    await processStream(userMessagesStream, 0).finally(cleanup)

    const final_time_completion_millsec = new Date().getTime() - start_time_millsec
@@ -841,4 +952,32 @@ export default class GeminiProvider extends BaseProvider {
  public generateImageByChat(): Promise<void> {
    throw new Error('Method not implemented.')
  }
+
+  public convertMcpTools<T>(mcpTools: MCPTool[]): T[] {
+    return mcpToolsToGeminiTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage = (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => {
+    if ('toolUseId' in mcpToolResponse && mcpToolResponse.toolUseId) {
+      return mcpToolCallResponseToGeminiMessage(mcpToolResponse, resp, isVisionModel(model))
+    } else if ('toolCallId' in mcpToolResponse) {
+      const toolCallOut = {
+        role: 'user',
+        parts: [
+          {
+            functionResponse: {
+              id: mcpToolResponse.toolCallId,
+              name: mcpToolResponse.tool.id,
+              response: {
+                output: !resp.isError ? resp.content : undefined,
+                error: resp.isError ? resp.content : undefined
+              }
+            }
+          }
+        ]
+      } satisfies Content
+      return toolCallOut
+    }
+    return
+  }
 }
--- a/src/renderer/src/providers/AiProvider/OpenAICompatibleProvider.ts
+++ b/src/renderer/src/providers/AiProvider/OpenAICompatibleProvider.ts
@@ -31,10 +31,13 @@ import {
  Assistant,
  EFFORT_RATIO,
  FileTypes,
+  MCPCallToolResponse,
+  MCPTool,
  MCPToolResponse,
  Model,
  Provider,
  Suggestion,
+  ToolCallResponse,
  Usage,
  WebSearchSource
 } from '@renderer/types'
@@ -48,7 +51,12 @@ import {
  convertLinksToOpenRouter,
  convertLinksToZhipu
 } from '@renderer/utils/linkConverter'
-import { mcpToolCallResponseToOpenAICompatibleMessage, parseAndCallTools } from '@renderer/utils/mcp-tools'
+import {
+  mcpToolCallResponseToOpenAICompatibleMessage,
+  mcpToolsToOpenAIChatTools,
+  openAIToolsToMcpTool,
+  parseAndCallTools
+} from '@renderer/utils/mcp-tools'
 import { findFileBlocks, findImageBlocks, getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { buildSystemPrompt } from '@renderer/utils/prompt'
 import { asyncGeneratorToReadableStream, readableStreamAsyncIterable } from '@renderer/utils/stream'
@@ -57,18 +65,22 @@ import OpenAI, { AzureOpenAI } from 'openai'
 import {
  ChatCompletionContentPart,
  ChatCompletionCreateParamsNonStreaming,
-  ChatCompletionMessageParam
+  ChatCompletionMessageParam,
+  ChatCompletionMessageToolCall,
+  ChatCompletionTool,
+  ChatCompletionToolMessageParam
 } from 'openai/resources'

 import { CompletionsParams } from '.'
-import OpenAIProvider from './OpenAIProvider'
+import { BaseOpenAiProvider } from './OpenAIProvider'

 // 1. 定义联合类型
 export type OpenAIStreamChunk =
  | { type: 'reasoning' | 'text-delta'; textDelta: string }
+  | { type: 'tool-calls'; delta: any }
  | { type: 'finish'; finishReason: any; usage: any; delta: any; chunk: any }

-export default class OpenAICompatibleProvider extends OpenAIProvider {
+export default class OpenAICompatibleProvider extends BaseOpenAiProvider {
  constructor(provider: Provider) {
    super(provider)

@@ -313,6 +325,24 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
    return {}
  }

+  public convertMcpTools<T>(mcpTools: MCPTool[]): T[] {
+    return mcpToolsToOpenAIChatTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage = (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => {
+    if ('toolUseId' in mcpToolResponse && mcpToolResponse.toolUseId) {
+      return mcpToolCallResponseToOpenAICompatibleMessage(mcpToolResponse, resp, isVisionModel(model))
+    } else if ('toolCallId' in mcpToolResponse && mcpToolResponse.toolCallId) {
+      const toolCallOut: ChatCompletionToolMessageParam = {
+        role: 'tool',
+        tool_call_id: mcpToolResponse.toolCallId,
+        content: JSON.stringify(resp.content)
+      }
+      return toolCallOut
+    }
+    return
+  }
+
  /**
   * Generate completions for the assistant
   * @param messages - The messages
@@ -330,7 +360,7 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
    const defaultModel = getDefaultModel()
    const model = assistant.model || defaultModel

-    const { contextCount, maxTokens, streamOutput } = getAssistantSettings(assistant)
+    const { contextCount, maxTokens, streamOutput, enableToolUse } = getAssistantSettings(assistant)
    const isEnabledBultinWebSearch = assistant.enableWebSearch
    messages = addImageFileToContents(messages)
    const enableReasoning =
@@ -344,7 +374,9 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
        content: `Formatting re-enabled${systemMessage ? '\n' + systemMessage.content : ''}`
      }
    }
-    if (mcpTools && mcpTools.length > 0) {
+    const { tools } = this.setupToolsConfig<ChatCompletionTool>({ mcpTools, model, enableToolUse })
+
+    if (this.useSystemPromptForTools) {
      systemMessage.content = buildSystemPrompt(systemMessage.content || '', mcpTools)
    }

@@ -379,53 +411,86 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {

    const toolResponses: MCPToolResponse[] = []

-    const processToolUses = async (content: string, idx: number) => {
-      const toolResults = await parseAndCallTools(
+    const processToolResults = async (toolResults: Awaited<ReturnType<typeof parseAndCallTools>>, idx: number) => {
+      if (toolResults.length === 0) return
+
+      toolResults.forEach((ts) => reqMessages.push(ts as ChatCompletionMessageParam))
+
+      console.debug('[tool] reqMessages before processing', model.id, reqMessages)
+      reqMessages = processReqMessages(model, reqMessages)
+      console.debug('[tool] reqMessages', model.id, reqMessages)
+
+      onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+      const newStream = await this.sdk.chat.completions
+        // @ts-ignore key is not typed
+        .create(
+          {
+            model: model.id,
+            messages: reqMessages,
+            temperature: this.getTemperature(assistant, model),
+            top_p: this.getTopP(assistant, model),
+            max_tokens: maxTokens,
+            keep_alive: this.keepAliveTime,
+            stream: isSupportStreamOutput(),
+            tools: !isEmpty(tools) ? tools : undefined,
+            ...getOpenAIWebSearchParams(assistant, model),
+            ...this.getReasoningEffort(assistant, model),
+            ...this.getProviderSpecificParameters(assistant, model),
+            ...this.getCustomParameters(assistant)
+          },
+          {
+            signal
+          }
+        )
+      await processStream(newStream, idx + 1)
+    }
+
+    const processToolCalls = async (mcpTools, toolCalls: ChatCompletionMessageToolCall[]) => {
+      const mcpToolResponses = toolCalls
+        .map((toolCall) => {
+          const mcpTool = openAIToolsToMcpTool(mcpTools, toolCall as ChatCompletionMessageToolCall)
+          if (!mcpTool) return undefined
+
+          const parsedArgs = (() => {
+            try {
+              return JSON.parse(toolCall.function.arguments)
+            } catch {
+              return toolCall.function.arguments
+            }
+          })()
+
+          return {
+            id: toolCall.id,
+            toolCallId: toolCall.id,
+            tool: mcpTool,
+            arguments: parsedArgs,
+            status: 'pending'
+          } as ToolCallResponse
+        })
+        .filter((t): t is ToolCallResponse => typeof t !== 'undefined')
+      return await parseAndCallTools(
+        mcpToolResponses,
+        toolResponses,
+        onChunk,
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
+      )
+    }
+
+    const processToolUses = async (content: string) => {
+      return await parseAndCallTools(
        content,
        toolResponses,
        onChunk,
-        idx,
-        mcpToolCallResponseToOpenAICompatibleMessage,
-        mcpTools,
-        isVisionModel(model)
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
      )
-
-      if (toolResults.length > 0) {
-        reqMessages.push({
-          role: 'assistant',
-          content: content
-        } as ChatCompletionMessageParam)
-        toolResults.forEach((ts) => reqMessages.push(ts as ChatCompletionMessageParam))
-
-        reqMessages = processReqMessages(model, reqMessages)
-        const newStream = await this.sdk.chat.completions
-          // @ts-ignore key is not typed
-          .create(
-            {
-              model: model.id,
-              messages: reqMessages,
-              temperature: this.getTemperature(assistant, model),
-              top_p: this.getTopP(assistant, model),
-              max_tokens: maxTokens,
-              keep_alive: this.keepAliveTime,
-              stream: isSupportStreamOutput(),
-              // tools: tools,
-              service_tier: this.getServiceTier(model),
-              ...getOpenAIWebSearchParams(assistant, model),
-              ...this.getReasoningEffort(assistant, model),
-              ...this.getProviderSpecificParameters(assistant, model),
-              ...this.getCustomParameters(assistant)
-            },
-            {
-              signal,
-              timeout: this.getTimeout(model)
-            }
-          )
-        await processStream(newStream, idx + 1)
-      }
    }

    const processStream = async (stream: any, idx: number) => {
+      const toolCalls: ChatCompletionMessageToolCall[] = []
      // Handle non-streaming case (already returns early, no change needed here)
      if (!isSupportStreamOutput()) {
        const time_completion_millsec = new Date().getTime() - start_time_millsec
@@ -439,10 +504,59 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
        // Create a synthetic usage object if stream.usage is undefined
        const finalUsage = stream.usage
        // Separate onChunk calls for text and usage/metrics
-        if (stream.choices[0].message?.content) {
-          onChunk({ type: ChunkType.TEXT_COMPLETE, text: stream.choices[0].message.content })
+        let content = ''
+        stream.choices.forEach((choice) => {
+          // reasoning
+          if (choice.message.reasoning) {
+            onChunk({ type: ChunkType.THINKING_DELTA, text: choice.message.reasoning })
+            onChunk({
+              type: ChunkType.THINKING_COMPLETE,
+              text: choice.message.reasoning,
+              thinking_millsec: time_completion_millsec
+            })
+          }
+          // text
+          if (choice.message.content) {
+            content += choice.message.content
+            onChunk({ type: ChunkType.TEXT_DELTA, text: choice.message.content })
+          }
+          // tool call
+          if (choice.message.tool_calls && choice.message.tool_calls.length) {
+            choice.message.tool_calls.forEach((t) => toolCalls.push(t))
+          }
+
+          reqMessages.push({
+            role: choice.message.role,
+            content: choice.message.content,
+            tool_calls: toolCalls.length
+              ? toolCalls.map((toolCall) => ({
+                  id: toolCall.id,
+                  function: {
+                    ...toolCall.function,
+                    arguments:
+                      typeof toolCall.function.arguments === 'string'
+                        ? toolCall.function.arguments
+                        : JSON.stringify(toolCall.function.arguments)
+                  },
+                  type: 'function'
+                }))
+              : undefined
+          })
+        })
+
+        if (content.length) {
+          onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
        }

+        const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+        if (toolCalls.length) {
+          toolResults.push(...(await processToolCalls(mcpTools, toolCalls)))
+        }
+        if (stream.choices[0].message?.content) {
+          toolResults.push(...(await processToolUses(stream.choices[0].message?.content)))
+        }
+        await processToolResults(toolResults, idx)
+
        // Always send usage and metrics data
        onChunk({ type: ChunkType.BLOCK_COMPLETE, response: { usage: finalUsage, metrics: finalMetrics } })
        return
@@ -486,6 +600,9 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
          if (delta?.content) {
            yield { type: 'text-delta', textDelta: delta.content }
          }
+          if (delta?.tool_calls) {
+            yield { type: 'tool-calls', delta: delta }
+          }

          const finishReason = chunk.choices[0]?.finish_reason
          if (!isEmpty(finishReason)) {
@@ -563,6 +680,25 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
            onChunk({ type: ChunkType.TEXT_DELTA, text: textDelta })
            break
          }
+          case 'tool-calls': {
+            chunk.delta.tool_calls.forEach((toolCall) => {
+              const { id, index, type, function: fun } = toolCall
+              if (id && type === 'function' && fun) {
+                const { name, arguments: args } = fun
+                toolCalls.push({
+                  id,
+                  function: {
+                    name: name || '',
+                    arguments: args || ''
+                  },
+                  type: 'function'
+                })
+              } else if (fun?.arguments) {
+                toolCalls[index].function.arguments += fun.arguments
+              }
+            })
+            break
+          }
          case 'finish': {
            const finishReason = chunk.finishReason
            const usage = chunk.usage
@@ -624,7 +760,33 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
                } as LLMWebSearchCompleteChunk)
              }
            }
-            await processToolUses(content, idx)
+            reqMessages.push({
+              role: 'assistant',
+              content: content,
+              tool_calls: toolCalls.length
+                ? toolCalls.map((toolCall) => ({
+                    id: toolCall.id,
+                    function: {
+                      ...toolCall.function,
+                      arguments:
+                        typeof toolCall.function.arguments === 'string'
+                          ? toolCall.function.arguments
+                          : JSON.stringify(toolCall.function.arguments)
+                    },
+                    type: 'function'
+                  }))
+                : undefined
+            })
+            let toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+            if (toolCalls.length) {
+              toolResults = await processToolCalls(mcpTools, toolCalls)
+            }
+            if (content.length) {
+              toolResults = toolResults.concat(await processToolUses(content))
+            }
+            if (toolResults.length) {
+              await processToolResults(toolResults, idx)
+            }
            onChunk({
              type: ChunkType.BLOCK_COMPLETE,
              response: {
@@ -657,7 +819,7 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
          max_tokens: maxTokens,
          keep_alive: this.keepAliveTime,
          stream: isSupportStreamOutput(),
-          // tools: tools,
+          tools: !isEmpty(tools) ? tools : undefined,
          service_tier: this.getServiceTier(model),
          ...getOpenAIWebSearchParams(assistant, model),
          ...this.getReasoningEffort(assistant, model),
--- a/src/renderer/src/providers/AiProvider/OpenAIProvider.ts
+++ b/src/renderer/src/providers/AiProvider/OpenAIProvider.ts
@@ -21,10 +21,13 @@ import {
  Assistant,
  FileTypes,
  GenerateImageParams,
+  MCPCallToolResponse,
+  MCPTool,
  MCPToolResponse,
  Model,
  Provider,
  Suggestion,
+  ToolCallResponse,
  Usage,
  WebSearchSource
 } from '@renderer/types'
@@ -33,7 +36,12 @@ import { Message } from '@renderer/types/newMessage'
 import { removeSpecialCharactersForTopicName } from '@renderer/utils'
 import { addImageFileToContents } from '@renderer/utils/formats'
 import { convertLinks } from '@renderer/utils/linkConverter'
-import { mcpToolCallResponseToOpenAIMessage, parseAndCallTools } from '@renderer/utils/mcp-tools'
+import {
+  mcpToolCallResponseToOpenAIMessage,
+  mcpToolsToOpenAIResponseTools,
+  openAIToolsToMcpTool,
+  parseAndCallTools
+} from '@renderer/utils/mcp-tools'
 import { findFileBlocks, findImageBlocks, getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { buildSystemPrompt } from '@renderer/utils/prompt'
 import { isEmpty, takeRight } from 'lodash'
@@ -45,7 +53,7 @@ import { FileLike, toFile } from 'openai/uploads'
 import { CompletionsParams } from '.'
 import BaseProvider from './BaseProvider'

-export default class OpenAIProvider extends BaseProvider {
+export abstract class BaseOpenAiProvider extends BaseProvider {
  protected sdk: OpenAI

  constructor(provider: Provider) {
@@ -61,6 +69,14 @@ export default class OpenAIProvider extends BaseProvider {
    })
  }

+  abstract convertMcpTools<T>(mcpTools: MCPTool[]): T[]
+
+  abstract mcpToolCallResponseToMessage: (
+    mcpToolResponse: MCPToolResponse,
+    resp: MCPCallToolResponse,
+    model: Model
+  ) => OpenAI.Responses.ResponseInputItem | ChatCompletionMessageParam | undefined
+
  /**
   * Extract the file content from the message
   * @param message - The message
@@ -91,16 +107,23 @@ export default class OpenAIProvider extends BaseProvider {
    return ''
  }

-  private async getReponseMessageParam(message: Message, model: Model): Promise<OpenAI.Responses.EasyInputMessage> {
+  private async getReponseMessageParam(message: Message, model: Model): Promise<OpenAI.Responses.ResponseInputItem> {
    const isVision = isVisionModel(model)
    const content = await this.getMessageContent(message)
    const fileBlocks = findFileBlocks(message)
    const imageBlocks = findImageBlocks(message)

    if (fileBlocks.length === 0 && imageBlocks.length === 0) {
-      return {
-        role: message.role === 'system' ? 'user' : message.role,
-        content: content ? [{ type: 'input_text', text: content }] : []
+      if (message.role === 'assistant') {
+        return {
+          role: 'assistant',
+          content: content
+        }
+      } else {
+        return {
+          role: message.role === 'system' ? 'user' : message.role,
+          content: content ? [{ type: 'input_text', text: content }] : []
+        } as OpenAI.Responses.EasyInputMessage
      }
    }

@@ -285,10 +308,8 @@ export default class OpenAIProvider extends BaseProvider {
    }
    const defaultModel = getDefaultModel()
    const model = assistant.model || defaultModel
-    const { contextCount, maxTokens, streamOutput } = getAssistantSettings(assistant)
-
+    const { contextCount, maxTokens, streamOutput, enableToolUse } = getAssistantSettings(assistant)
    const isEnabledBuiltinWebSearch = assistant.enableWebSearch
-    onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
    // 退回到 OpenAI 兼容模式
    if (isOpenAIWebSearch(model)) {
      const systemMessage = { role: 'system', content: assistant.prompt || '' }
@@ -387,7 +408,7 @@ export default class OpenAIProvider extends BaseProvider {
      })
      return
    }
-    const tools: OpenAI.Responses.Tool[] = []
+    let tools: OpenAI.Responses.Tool[] = []
    const toolChoices: OpenAI.Responses.ToolChoiceTypes = {
      type: 'web_search_preview'
    }
@@ -411,7 +432,15 @@ export default class OpenAIProvider extends BaseProvider {
      systemMessage.role = 'developer'
    }

-    if (mcpTools && mcpTools.length > 0) {
+    const { tools: extraTools } = this.setupToolsConfig<OpenAI.Responses.Tool>({
+      mcpTools,
+      model,
+      enableToolUse
+    })
+
+    tools = tools.concat(extraTools)
+
+    if (this.useSystemPromptForTools) {
      systemMessageInput.text = buildSystemPrompt(systemMessageInput.text || '', mcpTools)
    }
    systemMessageContent.push(systemMessageInput)
@@ -421,7 +450,7 @@ export default class OpenAIProvider extends BaseProvider {
    )

    onFilterMessages(_messages)
-    const userMessage: OpenAI.Responses.EasyInputMessage[] = []
+    const userMessage: OpenAI.Responses.ResponseInputItem[] = []
    for (const message of _messages) {
      userMessage.push(await this.getReponseMessageParam(message, model))
    }
@@ -434,7 +463,7 @@ export default class OpenAIProvider extends BaseProvider {
    const { signal } = abortController

    // 当 systemMessage 内容为空时不发送 systemMessage
-    let reqMessages: OpenAI.Responses.EasyInputMessage[]
+    let reqMessages: OpenAI.Responses.ResponseInput
    if (!systemMessage.content) {
      reqMessages = [...userMessage]
    } else {
@@ -443,48 +472,84 @@ export default class OpenAIProvider extends BaseProvider {

    const toolResponses: MCPToolResponse[] = []

-    const processToolUses = async (content: string, idx: number) => {
-      const toolResults = await parseAndCallTools(
+    const processToolResults = async (toolResults: Awaited<ReturnType<typeof parseAndCallTools>>, idx: number) => {
+      if (toolResults.length === 0) return
+
+      toolResults.forEach((ts) => reqMessages.push(ts as OpenAI.Responses.EasyInputMessage))
+
+      onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
+      const stream = await this.sdk.responses.create(
+        {
+          model: model.id,
+          input: reqMessages,
+          temperature: this.getTemperature(assistant, model),
+          top_p: this.getTopP(assistant, model),
+          max_output_tokens: maxTokens,
+          stream: streamOutput,
+          tools: !isEmpty(tools) ? tools : undefined,
+          service_tier: this.getServiceTier(model),
+          ...this.getResponseReasoningEffort(assistant, model),
+          ...this.getCustomParameters(assistant)
+        },
+        {
+          signal,
+          timeout: this.getTimeout(model)
+        }
+      )
+      await processStream(stream, idx + 1)
+    }
+
+    const processToolCalls = async (mcpTools, toolCalls: OpenAI.Responses.ResponseFunctionToolCall[]) => {
+      const mcpToolResponses = toolCalls
+        .map((toolCall) => {
+          const mcpTool = openAIToolsToMcpTool(mcpTools, toolCall as OpenAI.Responses.ResponseFunctionToolCall)
+          if (!mcpTool) return undefined
+
+          const parsedArgs = (() => {
+            try {
+              return JSON.parse(toolCall.arguments)
+            } catch {
+              return toolCall.arguments
+            }
+          })()
+
+          return {
+            id: toolCall.call_id,
+            toolCallId: toolCall.call_id,
+            tool: mcpTool,
+            arguments: parsedArgs,
+            status: 'pending'
+          } as ToolCallResponse
+        })
+        .filter((t): t is ToolCallResponse => typeof t !== 'undefined')
+
+      return await parseAndCallTools<OpenAI.Responses.ResponseInputItem | ChatCompletionMessageParam>(
+        mcpToolResponses,
+        toolResponses,
+        onChunk,
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
+      )
+    }
+
+    const processToolUses = async (content: string) => {
+      return await parseAndCallTools(
        content,
        toolResponses,
        onChunk,
-        idx,
-        mcpToolCallResponseToOpenAIMessage,
-        mcpTools,
-        isVisionModel(model)
+        this.mcpToolCallResponseToMessage,
+        model,
+        mcpTools
      )
-
-      if (toolResults.length > 0) {
-        reqMessages.push({
-          role: 'assistant',
-          content: content
-        })
-        toolResults.forEach((ts) => reqMessages.push(ts as OpenAI.Responses.EasyInputMessage))
-        const newStream = await this.sdk.responses.create(
-          {
-            model: model.id,
-            input: reqMessages,
-            temperature: this.getTemperature(assistant, model),
-            top_p: this.getTopP(assistant, model),
-            max_output_tokens: maxTokens,
-            stream: true,
-            service_tier: this.getServiceTier(model),
-            ...this.getResponseReasoningEffort(assistant, model),
-            ...this.getCustomParameters(assistant)
-          },
-          {
-            signal,
-            timeout: this.getTimeout(model)
-          }
-        )
-        await processStream(newStream, idx + 1)
-      }
    }

    const processStream = async (
      stream: Stream<OpenAI.Responses.ResponseStreamEvent> | OpenAI.Responses.Response,
      idx: number
    ) => {
+      const toolCalls: OpenAI.Responses.ResponseFunctionToolCall[] = []
+
      if (!streamOutput) {
        const nonStream = stream as OpenAI.Responses.Response
        const time_completion_millsec = new Date().getTime() - start_time_millsec
@@ -502,11 +567,15 @@ export default class OpenAIProvider extends BaseProvider {
          prompt_tokens: nonStream.usage?.input_tokens || 0,
          total_tokens
        }
+        let content = ''
+
        for (const output of nonStream.output) {
          switch (output.type) {
            case 'message':
              if (output.content[0].type === 'output_text') {
+                onChunk({ type: ChunkType.TEXT_DELTA, text: output.content[0].text })
                onChunk({ type: ChunkType.TEXT_COMPLETE, text: output.content[0].text })
+                content += output.content[0].text
                if (output.content[0].annotations && output.content[0].annotations.length > 0) {
                  onChunk({
                    type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
@@ -525,8 +594,32 @@ export default class OpenAIProvider extends BaseProvider {
                thinking_millsec: new Date().getTime() - start_time_millsec
              })
              break
+            case 'function_call':
+              toolCalls.push(output)
          }
        }
+
+        if (content) {
+          reqMessages.push({
+            role: 'assistant',
+            content: content
+          })
+        }
+        if (toolCalls.length) {
+          toolCalls.forEach((toolCall) => {
+            reqMessages.push(toolCall)
+          })
+        }
+
+        const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+        if (toolCalls.length) {
+          toolResults.push(...(await processToolCalls(mcpTools, toolCalls)))
+        }
+        if (content.length) {
+          toolResults.push(...(await processToolUses(content)))
+        }
+        await processToolResults(toolResults, idx)
+
        onChunk({
          type: ChunkType.BLOCK_COMPLETE,
          response: {
@@ -537,6 +630,9 @@ export default class OpenAIProvider extends BaseProvider {
        return
      }
      let content = ''
+
+      const outputItems: OpenAI.Responses.ResponseOutputItem[] = []
+
      let lastUsage: Usage | undefined = undefined
      let final_time_completion_millsec_delta = 0
      for await (const chunk of stream as Stream<OpenAI.Responses.ResponseStreamEvent>) {
@@ -547,6 +643,12 @@ export default class OpenAIProvider extends BaseProvider {
          case 'response.created':
            time_first_token_millsec = new Date().getTime()
            break
+          case 'response.output_item.added':
+            if (chunk.item.type === 'function_call') {
+              outputItems.push(chunk.item)
+            }
+            break
+
          case 'response.reasoning_summary_text.delta':
            onChunk({
              type: ChunkType.THINKING_DELTA,
@@ -579,6 +681,21 @@ export default class OpenAIProvider extends BaseProvider {
              text: content
            })
            break
+          case 'response.function_call_arguments.done': {
+            const outputItem: OpenAI.Responses.ResponseOutputItem | undefined = outputItems.find(
+              (item) => item.id === chunk.item_id
+            )
+            if (outputItem) {
+              if (outputItem.type === 'function_call') {
+                toolCalls.push({
+                  ...outputItem,
+                  arguments: chunk.arguments
+                })
+              }
+            }
+
+            break
+          }
          case 'response.content_part.done':
            if (chunk.part.type === 'output_text' && chunk.part.annotations && chunk.part.annotations.length > 0) {
              onChunk({
@@ -615,9 +732,31 @@ export default class OpenAIProvider extends BaseProvider {
            })
            break
        }
+
+        // --- End of Incremental onChunk calls ---
+      } // End of for await loop
+      if (content) {
+        reqMessages.push({
+          role: 'assistant',
+          content: content
+        })
+      }
+      if (toolCalls.length) {
+        toolCalls.forEach((toolCall) => {
+          reqMessages.push(toolCall)
+        })
      }

-      await processToolUses(content, idx)
+      // Call processToolUses AFTER the loop finishes processing the main stream content
+      // Note: parseAndCallTools inside processToolUses should handle its own onChunk for tool responses
+      const toolResults: Awaited<ReturnType<typeof parseAndCallTools>> = []
+      if (toolCalls.length) {
+        toolResults.push(...(await processToolCalls(mcpTools, toolCalls)))
+      }
+      if (content) {
+        toolResults.push(...(await processToolUses(content)))
+      }
+      await processToolResults(toolResults, idx)

      onChunk({
        type: ChunkType.BLOCK_COMPLETE,
@@ -632,6 +771,7 @@ export default class OpenAIProvider extends BaseProvider {
      })
    }

+    onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
    const stream = await this.sdk.responses.create(
      {
        model: model.id,
@@ -1081,3 +1221,31 @@ export default class OpenAIProvider extends BaseProvider {
    return data.data[0].embedding.length
  }
 }
+
+export default class OpenAIProvider extends BaseOpenAiProvider {
+  constructor(provider: Provider) {
+    super(provider)
+  }
+
+  public convertMcpTools<T>(mcpTools: MCPTool[]) {
+    return mcpToolsToOpenAIResponseTools(mcpTools) as T[]
+  }
+
+  public mcpToolCallResponseToMessage = (
+    mcpToolResponse: MCPToolResponse,
+    resp: MCPCallToolResponse,
+    model: Model
+  ): OpenAI.Responses.ResponseInputItem | undefined => {
+    if ('toolUseId' in mcpToolResponse && mcpToolResponse.toolUseId) {
+      return mcpToolCallResponseToOpenAIMessage(mcpToolResponse, resp, isVisionModel(model))
+    } else if ('toolCallId' in mcpToolResponse && mcpToolResponse.toolCallId) {
+      const toolCallOut: OpenAI.Responses.ResponseInputItem = {
+        type: 'function_call_output',
+        call_id: mcpToolResponse.toolCallId,
+        output: JSON.stringify(resp.content)
+      }
+      return toolCallOut
+    }
+    return
+  }
+}
--- a/src/renderer/src/services/AssistantService.ts
+++ b/src/renderer/src/services/AssistantService.ts
@@ -107,6 +107,7 @@ export const getAssistantSettings = (assistant: Assistant): AssistantSettings =>
    enableMaxTokens: assistant?.settings?.enableMaxTokens ?? false,
    maxTokens: getAssistantMaxTokens(),
    streamOutput: assistant?.settings?.streamOutput ?? true,
+    enableToolUse: assistant?.settings?.enableToolUse ?? false,
    hideMessages: assistant?.settings?.hideMessages ?? false,
    defaultModel: assistant?.defaultModel ?? undefined,
    customParameters: assistant?.settings?.customParameters ?? []
--- a/src/renderer/src/store/index.ts
+++ b/src/renderer/src/store/index.ts
@@ -46,7 +46,7 @@ const persistedReducer = persistReducer(
  {
    key: 'cherry-studio',
    storage,
-    version: 99,
+    version: 98,
    blacklist: ['runtime', 'messages', 'messageBlocks'],
    migrate
  },
--- a/src/renderer/src/store/llm.ts
+++ b/src/renderer/src/store/llm.ts
@@ -476,16 +476,6 @@ export const INITIAL_PROVIDERS: Provider[] = [
    models: SYSTEM_MODELS.voyageai,
    isSystem: true,
    enabled: false
-  },
-  {
-    id: 'paratera',
-    name: 'Paratera AI',
-    type: 'openai-compatible',
-    apiKey: '',
-    apiHost: 'https://llmapi.paratera.com',
-    models: SYSTEM_MODELS.paratera,
-    isSystem: true,
-    enabled: false
  }
 ]

--- a/src/renderer/src/store/migrate.ts
+++ b/src/renderer/src/store/migrate.ts
@@ -1248,15 +1248,6 @@ const migrateConfig = {
          provider.type = 'openai-compatible'
        }
      })
-      return state
-    } catch (error) {
-      return state
-    }
-  },
-  '99': (state: RootState) => {
-    try {
-      addProvider(state, 'paratera')
-
      return state
    } catch (error) {
      return state
--- a/src/renderer/src/store/thunk/messageThunk.ts
+++ b/src/renderer/src/store/thunk/messageThunk.ts
@@ -427,7 +427,17 @@ const fetchAndProcessAssistantResponseImpl = async (
        }
      },
      onToolCallInProgress: (toolResponse: MCPToolResponse) => {
-        if (toolResponse.status === 'invoking') {
+        if (lastBlockType === MessageBlockType.UNKNOWN && lastBlockId) {
+          lastBlockType = MessageBlockType.TOOL
+          const changes = {
+            type: MessageBlockType.TOOL,
+            status: MessageBlockStatus.PROCESSING,
+            metadata: { rawMcpToolResponse: toolResponse }
+          }
+          dispatch(updateOneBlock({ id: lastBlockId, changes }))
+          saveUpdatedBlockToDB(lastBlockId, assistantMsgId, topicId, getState)
+          toolCallIdToBlockIdMap.set(toolResponse.id, lastBlockId)
+        } else if (toolResponse.status === 'invoking') {
          const toolBlock = createToolBlock(assistantMsgId, toolResponse.id, {
            toolName: toolResponse.tool.name,
            status: MessageBlockStatus.PROCESSING,
--- a/src/renderer/src/types/index.ts
+++ b/src/renderer/src/types/index.ts
@@ -55,6 +55,7 @@ export type AssistantSettings = {
  maxTokens: number | undefined
  enableMaxTokens: boolean
  streamOutput: boolean
+  enableToolUse: boolean
  hideMessages: boolean
  defaultModel?: Model
  customParameters?: AssistantSettingCustomParameters[]
@@ -570,13 +571,25 @@ export interface MCPConfig {
  servers: MCPServer[]
 }

-export interface MCPToolResponse {
-  id: string // tool call id, it should be unique
-  tool: MCPTool // tool info
+interface BaseToolResponse {
+  id: string // unique id
+  tool: MCPTool
+  arguments: Record<string, unknown> | undefined
  status: string // 'invoking' | 'done'
  response?: any
 }

+export interface ToolUseResponse extends BaseToolResponse {
+  toolUseId: string
+}
+
+export interface ToolCallResponse extends BaseToolResponse {
+  // gemini tool call id might be undefined
+  toolCallId?: string
+}
+
+export type MCPToolResponse = ToolUseResponse | ToolCallResponse
+
 export interface MCPToolResultContent {
  type: 'text' | 'image' | 'audio' | 'resource'
  text?: string
@@ -586,6 +599,7 @@ export interface MCPToolResultContent {
    uri?: string
    text?: string
    mimeType?: string
+    blob?: string
  }
 }

--- a/src/renderer/src/utils/mcp-tools.ts
+++ b/src/renderer/src/utils/mcp-tools.ts
@@ -1,18 +1,31 @@
-import { ContentBlockParam, ToolUnion, ToolUseBlock } from '@anthropic-ai/sdk/resources'
-import { MessageParam } from '@anthropic-ai/sdk/resources'
-import { Content, FunctionCall, Part } from '@google/genai'
+import {
+  ContentBlockParam,
+  MessageParam,
+  ToolResultBlockParam,
+  ToolUnion,
+  ToolUseBlock
+} from '@anthropic-ai/sdk/resources'
+import { Content, FunctionCall, Part, Tool, Type as GeminiSchemaType } from '@google/genai'
+import { isVisionModel } from '@renderer/config/models'
 import store from '@renderer/store'
 import { addMCPServer } from '@renderer/store/mcp'
-import { MCPCallToolResponse, MCPServer, MCPTool, MCPToolResponse } from '@renderer/types'
+import { MCPCallToolResponse, MCPServer, MCPTool, MCPToolResponse, Model, ToolUseResponse } from '@renderer/types'
 import type { MCPToolCompleteChunk, MCPToolInProgressChunk } from '@renderer/types/chunk'
 import { ChunkType } from '@renderer/types/chunk'
+import { isArray, isObject, pull, transform } from 'lodash'
 import { nanoid } from 'nanoid'
 import OpenAI from 'openai'
-import { ChatCompletionContentPart, ChatCompletionMessageParam, ChatCompletionMessageToolCall } from 'openai/resources'
+import {
+  ChatCompletionContentPart,
+  ChatCompletionMessageParam,
+  ChatCompletionMessageToolCall,
+  ChatCompletionTool
+} from 'openai/resources'

 import { CompletionsParams } from '../providers/AiProvider'

 const MCP_AUTO_INSTALL_SERVER_NAME = '@cherry/mcp-auto-install'
+const EXTRA_SCHEMA_KEYS = ['schema', 'headers']

 // const ensureValidSchema = (obj: Record<string, any>) => {
 //   // Filter out unsupported keys for Gemini
@@ -153,77 +166,116 @@ const MCP_AUTO_INSTALL_SERVER_NAME = '@cherry/mcp-auto-install'
 //   return processedProperties
 // }

-// export function mcpToolsToOpenAITools(mcpTools: MCPTool[]): Array<ChatCompletionTool> {
-//   return mcpTools.map((tool) => ({
-//     type: 'function',
-//     name: tool.name,
-//     function: {
-//       name: tool.id,
-//       description: tool.description,
-//       parameters: {
-//         type: 'object',
-//         properties: filterPropertieAttributes(tool)
-//       }
-//     }
-//   }))
-// }
-
-export function openAIToolsToMcpTool(
-  mcpTools: MCPTool[] | undefined,
-  llmTool: ChatCompletionMessageToolCall
-): MCPTool | undefined {
-  if (!mcpTools) {
-    return undefined
+export function filterProperties(
+  properties: Record<string, any> | string | number | boolean | Array<Record<string, any> | string | number | boolean>,
+  supportedKeys: string[]
+) {
+  // If it is an array, recursively process each element
+  if (isArray(properties)) {
+    return properties.map((item) => filterProperties(item, supportedKeys))
  }

-  const tool = mcpTools.find(
-    (mcptool) => mcptool.id === llmTool.function.name || mcptool.name === llmTool.function.name
-  )
+  // If it is an object, recursively process each property
+  if (isObject(properties)) {
+    return transform(
+      properties,
+      (result, value, key) => {
+        if (key === 'properties') {
+          result[key] = transform(value, (acc, v, k) => {
+            acc[k] = filterProperties(v, supportedKeys)
+          })

-  if (!tool) {
-    console.warn('No MCP Tool found for tool call:', llmTool)
-    return undefined
+          result['additionalProperties'] = false
+          result['required'] = pull(Object.keys(value), ...EXTRA_SCHEMA_KEYS)
+        } else if (key === 'oneOf') {
+          // openai only supports anyOf
+          result['anyOf'] = filterProperties(value, supportedKeys)
+        } else if (supportedKeys.includes(key)) {
+          result[key] = filterProperties(value, supportedKeys)
+          if (key === 'type' && value === 'object') {
+            result['additionalProperties'] = false
+          }
+        }
+      },
+      {}
+    )
  }

-  console.log(
-    `[MCP] OpenAI Tool to MCP Tool: ${tool.serverName} ${tool.name}`,
-    tool,
-    'args',
-    llmTool.function.arguments
-  )
-  // use this to parse the arguments and avoid parsing errors
-  let args: any = {}
-  try {
-    args = JSON.parse(llmTool.function.arguments)
-  } catch (e) {
-    console.error('Error parsing arguments', e)
-  }
-
-  return {
-    id: tool.id,
-    serverId: tool.serverId,
-    serverName: tool.serverName,
-    name: tool.name,
-    description: tool.description,
-    inputSchema: args
-  }
+  // Return other types directly (e.g., string, number, etc.)
+  return properties
 }

-export async function callMCPTool(tool: MCPTool): Promise<MCPCallToolResponse> {
-  console.log(`[MCP] Calling Tool: ${tool.serverName} ${tool.name}`, tool)
+export function mcpToolsToOpenAIResponseTools(mcpTools: MCPTool[]): OpenAI.Responses.Tool[] {
+  const schemaKeys = ['type', 'description', 'items', 'enum', 'additionalProperties', 'anyof']
+  return mcpTools.map(
+    (tool) =>
+      ({
+        type: 'function',
+        name: tool.id,
+        parameters: {
+          type: 'object',
+          properties: filterProperties(tool.inputSchema, schemaKeys).properties,
+          required: pull(Object.keys(tool.inputSchema.properties), ...EXTRA_SCHEMA_KEYS),
+          additionalProperties: false
+        },
+        strict: true
+      }) satisfies OpenAI.Responses.Tool
+  )
+}
+
+export function mcpToolsToOpenAIChatTools(mcpTools: MCPTool[]): Array<ChatCompletionTool> {
+  return mcpTools.map(
+    (tool) =>
+      ({
+        type: 'function',
+        function: {
+          name: tool.id,
+          description: tool.description,
+          parameters: {
+            type: 'object',
+            properties: tool.inputSchema.properties,
+            required: tool.inputSchema.required
+          }
+        }
+      }) as ChatCompletionTool
+  )
+}
+
+export function openAIToolsToMcpTool(
+  mcpTools: MCPTool[],
+  toolCall: OpenAI.Responses.ResponseFunctionToolCall | ChatCompletionMessageToolCall
+): MCPTool | undefined {
+  const tool = mcpTools.find((mcpTool) => {
+    if ('name' in toolCall) {
+      return mcpTool.id === toolCall.name || mcpTool.name === toolCall.name
+    } else {
+      return mcpTool.id === toolCall.function.name || mcpTool.name === toolCall.function.name
+    }
+  })
+
+  if (!tool) {
+    console.warn('No MCP Tool found for tool call:', toolCall)
+    return undefined
+  }
+
+  return tool
+}
+
+export async function callMCPTool(toolResponse: MCPToolResponse): Promise<MCPCallToolResponse> {
+  console.log(`[MCP] Calling Tool: ${toolResponse.tool.serverName} ${toolResponse.tool.name}`, toolResponse.tool)
  try {
-    const server = getMcpServerByTool(tool)
+    const server = getMcpServerByTool(toolResponse.tool)

    if (!server) {
-      throw new Error(`Server not found: ${tool.serverName}`)
+      throw new Error(`Server not found: ${toolResponse.tool.serverName}`)
    }

    const resp = await window.api.mcp.callTool({
      server,
-      name: tool.name,
-      args: tool.inputSchema
+      name: toolResponse.tool.name,
+      args: toolResponse.arguments
    })
-    if (tool.serverName === MCP_AUTO_INSTALL_SERVER_NAME) {
+    if (toolResponse.tool.serverName === MCP_AUTO_INSTALL_SERVER_NAME) {
      if (resp.data) {
        const mcpServer: MCPServer = {
          id: `f${nanoid()}`,
@@ -241,16 +293,16 @@ export async function callMCPTool(tool: MCPTool): Promise<MCPCallToolResponse> {
      }
    }

-    console.log(`[MCP] Tool called: ${tool.serverName} ${tool.name}`, resp)
+    console.log(`[MCP] Tool called: ${toolResponse.tool.serverName} ${toolResponse.tool.name}`, resp)
    return resp
  } catch (e) {
-    console.error(`[MCP] Error calling Tool: ${tool.serverName} ${tool.name}`, e)
+    console.error(`[MCP] Error calling Tool: ${toolResponse.tool.serverName} ${toolResponse.tool.name}`, e)
    return Promise.resolve({
      isError: true,
      content: [
        {
          type: 'text',
-          text: `Error calling tool ${tool.name}: ${e instanceof Error ? e.stack || e.message || 'No error details available' : JSON.stringify(e)}`
+          text: `Error calling tool ${toolResponse.tool.name}: ${e instanceof Error ? e.stack || e.message || 'No error details available' : JSON.stringify(e)}`
        }
      ]
    })
@@ -262,7 +314,7 @@ export function mcpToolsToAnthropicTools(mcpTools: MCPTool[]): Array<ToolUnion>
    const t: ToolUnion = {
      name: tool.id,
      description: tool.description,
-      // @ts-ignore no check
+      // @ts-ignore ignore type as it it unknow
      input_schema: tool.inputSchema
    }
    return t
@@ -275,53 +327,68 @@ export function anthropicToolUseToMcpTool(mcpTools: MCPTool[] | undefined, toolU
  if (!tool) {
    return undefined
  }
-  // @ts-ignore ignore type as it it unknow
-  tool.inputSchema = toolUse.input
  return tool
 }

-// export function mcpToolsToGeminiTools(mcpTools: MCPTool[] | undefined): geminiTool[] {
-//   if (!mcpTools || mcpTools.length === 0) {
-//     // No tools available
-//     return []
-//   }
-//   const functions: FunctionDeclaration[] = []
-
-//   for (const tool of mcpTools) {
-//     const properties = filterPropertieAttributes(tool, true)
-//     const functionDeclaration: FunctionDeclaration = {
-//       name: tool.id,
-//       description: tool.description,
-//       parameters: {
-//         type: SchemaType.OBJECT,
-//         properties:
-//           Object.keys(properties).length > 0
-//             ? Object.fromEntries(
-//                 Object.entries(properties).map(([key, value]) => [key, ensureValidSchema(value as Record<string, any>)])
-//               )
-//             : { _empty: { type: SchemaType.STRING } as SimpleStringSchema }
-//       } as FunctionDeclarationSchema
-//     }
-//     functions.push(functionDeclaration)
-//   }
-//   const tool: geminiTool = {
-//     functionDeclarations: functions
-//   }
-//   return [tool]
-// }
+/**
+ * @param mcpTools
+ * @returns
+ */
+export function mcpToolsToGeminiTools(mcpTools: MCPTool[]): Tool[] {
+  /**
+   * @typedef {import('@google/genai').Schema} Schema
+   */
+  const schemaKeys = [
+    'example',
+    'pattern',
+    'default',
+    'maxLength',
+    'minLength',
+    'minProperties',
+    'maxProperties',
+    'anyOf',
+    'description',
+    'enum',
+    'format',
+    'items',
+    'maxItems',
+    'maximum',
+    'minItems',
+    'minimum',
+    'nullable',
+    'properties',
+    'propertyOrdering',
+    'required',
+    'title',
+    'type'
+  ]
+  return [
+    {
+      functionDeclarations: mcpTools?.map((tool) => {
+        return {
+          name: tool.id,
+          description: tool.description,
+          parameters: {
+            type: GeminiSchemaType.OBJECT,
+            properties: filterProperties(tool.inputSchema, schemaKeys).properties,
+            required: tool.inputSchema.required
+          }
+        }
+      })
+    }
+  ]
+}

 export function geminiFunctionCallToMcpTool(
  mcpTools: MCPTool[] | undefined,
-  fcall: FunctionCall | undefined
+  toolCall: FunctionCall | undefined
 ): MCPTool | undefined {
-  if (!fcall) return undefined
+  if (!toolCall) return undefined
  if (!mcpTools) return undefined
-  const tool = mcpTools.find((tool) => tool.id === fcall.name)
+  const tool = mcpTools.find((tool) => tool.id === toolCall.name)
  if (!tool) {
    return undefined
  }
-  // @ts-ignore schema is not a valid property
-  tool.inputSchema = fcall.args
  return tool
 }

@@ -368,13 +435,13 @@ export function getMcpServerByTool(tool: MCPTool) {
  return servers.find((s) => s.id === tool.serverId)
 }

-export function parseToolUse(content: string, mcpTools: MCPTool[]): MCPToolResponse[] {
+export function parseToolUse(content: string, mcpTools: MCPTool[]): ToolUseResponse[] {
  if (!content || !mcpTools || mcpTools.length === 0) {
    return []
  }
  const toolUsePattern =
    /<tool_use>([\s\S]*?)<name>([\s\S]*?)<\/name>([\s\S]*?)<arguments>([\s\S]*?)<\/arguments>([\s\S]*?)<\/tool_use>/g
-  const tools: MCPToolResponse[] = []
+  const tools: ToolUseResponse[] = []
  let match
  let idx = 0
  // Find all tool use blocks
@@ -401,10 +468,9 @@ export function parseToolUse(content: string, mcpTools: MCPTool[]): MCPToolRespo
    // Add to tools array
    tools.push({
      id: `${toolName}-${idx++}`, // Unique ID for each tool use
-      tool: {
-        ...mcpTool,
-        inputSchema: parsedArgs
-      },
+      toolUseId: mcpTool.id,
+      tool: mcpTool,
+      arguments: parsedArgs,
      status: 'pending'
    })

@@ -414,36 +480,69 @@ export function parseToolUse(content: string, mcpTools: MCPTool[]): MCPToolRespo
  return tools
 }

-export async function parseAndCallTools(
-  content: string,
-  toolResponses: MCPToolResponse[],
+export async function parseAndCallTools<R>(
+  tools: MCPToolResponse[],
+  allToolResponses: MCPToolResponse[],
  onChunk: CompletionsParams['onChunk'],
-  idx: number,
-  convertToMessage: (
-    toolCallId: string,
-    resp: MCPCallToolResponse,
-    isVisionModel: boolean
-  ) => ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.EasyInputMessage,
-  mcpTools?: MCPTool[],
-  isVisionModel: boolean = false
-): Promise<(ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.EasyInputMessage)[]> {
-  const toolResults: (ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.EasyInputMessage)[] = []
-  // process tool use
-  const tools = parseToolUse(content, mcpTools || [])
-  if (!tools || tools.length === 0) {
+  convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined,
+  model: Model,
+  mcpTools?: MCPTool[]
+): Promise<
+  (ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.ResponseInputItem | ToolResultBlockParam)[]
+>
+
+export async function parseAndCallTools<R>(
+  content: string,
+  allToolResponses: MCPToolResponse[],
+  onChunk: CompletionsParams['onChunk'],
+  convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined,
+  model: Model,
+  mcpTools?: MCPTool[]
+): Promise<
+  (ChatCompletionMessageParam | MessageParam | Content | OpenAI.Responses.ResponseInputItem | ToolResultBlockParam)[]
+>
+
+export async function parseAndCallTools<R>(
+  content: string | MCPToolResponse[],
+  allToolResponses: MCPToolResponse[],
+  onChunk: CompletionsParams['onChunk'],
+  convertToMessage: (mcpToolResponse: MCPToolResponse, resp: MCPCallToolResponse, model: Model) => R | undefined,
+  model: Model,
+  mcpTools?: MCPTool[]
+): Promise<R[]> {
+  const toolResults: R[] = []
+  let curToolResponses: MCPToolResponse[] = []
+  if (Array.isArray(content)) {
+    curToolResponses = content
+  } else {
+    // process tool use
+    curToolResponses = parseToolUse(content, mcpTools || [])
+  }
+  if (!curToolResponses || curToolResponses.length === 0) {
    return toolResults
  }
-  for (let i = 0; i < tools.length; i++) {
-    const tool = tools[i]
-    upsertMCPToolResponse(toolResponses, { id: `${tool.id}-${idx}-${i}`, tool: tool.tool, status: 'invoking' }, onChunk)
+  for (let i = 0; i < curToolResponses.length; i++) {
+    const toolResponse = curToolResponses[i]
+    upsertMCPToolResponse(
+      allToolResponses,
+      {
+        ...toolResponse,
+        status: 'invoking'
+      },
+      onChunk
+    )
  }

-  const toolPromises = tools.map(async (tool, i) => {
+  const toolPromises = curToolResponses.map(async (toolResponse) => {
    const images: string[] = []
-    const toolCallResponse = await callMCPTool(tool.tool)
+    const toolCallResponse = await callMCPTool(toolResponse)
    upsertMCPToolResponse(
-      toolResponses,
-      { id: `${tool.id}-${idx}-${i}`, tool: tool.tool, status: 'done', response: toolCallResponse },
+      allToolResponses,
+      {
+        ...toolResponse,
+        status: 'done',
+        response: toolCallResponse
+      },
      onChunk
    )

@@ -466,15 +565,15 @@ export async function parseAndCallTools(
      })
    }

-    return convertToMessage(tool.tool.id, toolCallResponse, isVisionModel)
+    return convertToMessage(toolResponse, toolCallResponse, model)
  })

-  toolResults.push(...(await Promise.all(toolPromises)))
+  toolResults.push(...(await Promise.all(toolPromises)).filter((t) => typeof t !== 'undefined'))
  return toolResults
 }

 export function mcpToolCallResponseToOpenAICompatibleMessage(
-  toolCallId: string,
+  mcpToolResponse: MCPToolResponse,
  resp: MCPCallToolResponse,
  isVisionModel: boolean = false
 ): ChatCompletionMessageParam {
@@ -488,7 +587,7 @@ export function mcpToolCallResponseToOpenAICompatibleMessage(
    const content: ChatCompletionContentPart[] = [
      {
        type: 'text',
-        text: `Here is the result of tool call ${toolCallId}:`
+        text: `Here is the result of mcp tool use \`${mcpToolResponse.tool.name}\`:`
      }
    ]

@@ -541,7 +640,7 @@ export function mcpToolCallResponseToOpenAICompatibleMessage(
 }

 export function mcpToolCallResponseToOpenAIMessage(
-  toolCallId: string,
+  mcpToolResponse: MCPToolResponse,
  resp: MCPCallToolResponse,
  isVisionModel: boolean = false
 ): OpenAI.Responses.EasyInputMessage {
@@ -555,7 +654,7 @@ export function mcpToolCallResponseToOpenAIMessage(
    const content: OpenAI.Responses.ResponseInputContent[] = [
      {
        type: 'input_text',
-        text: `Here is the result of tool call ${toolCallId}:`
+        text: `Here is the result of mcp tool use \`${mcpToolResponse.tool.name}\`:`
      }
    ]

@@ -597,9 +696,9 @@ export function mcpToolCallResponseToOpenAIMessage(
 }

 export function mcpToolCallResponseToAnthropicMessage(
-  toolCallId: string,
+  mcpToolResponse: MCPToolResponse,
  resp: MCPCallToolResponse,
-  isVisionModel: boolean = false
+  model: Model
 ): MessageParam {
  const message = {
    role: 'user'
@@ -610,10 +709,10 @@ export function mcpToolCallResponseToAnthropicMessage(
    const content: ContentBlockParam[] = [
      {
        type: 'text',
-        text: `Here is the result of tool call ${toolCallId}:`
+        text: `Here is the result of mcp tool use \`${mcpToolResponse.tool.name}\`:`
      }
    ]
-    if (isVisionModel) {
+    if (isVisionModel(model)) {
      for (const item of resp.content) {
        switch (item.type) {
          case 'text':
@@ -665,7 +764,7 @@ export function mcpToolCallResponseToAnthropicMessage(
 }

 export function mcpToolCallResponseToGeminiMessage(
-  toolCallId: string,
+  mcpToolResponse: MCPToolResponse,
  resp: MCPCallToolResponse,
  isVisionModel: boolean = false
 ): Content {
@@ -682,7 +781,7 @@ export function mcpToolCallResponseToGeminiMessage(
  } else {
    const parts: Part[] = [
      {
-        text: `Here is the result of tool call ${toolCallId}:`
+        text: `Here is the result of mcp tool use \`${mcpToolResponse.tool.name}\`:`
      }
    ]
    if (isVisionModel) {
--- a/src/renderer/src/utils/prompt.ts
+++ b/src/renderer/src/utils/prompt.ts
@@ -147,7 +147,7 @@ ${availableTools}
 </tools>`
 }

-export const buildSystemPrompt = (userSystemPrompt: string, tools: MCPTool[]): string => {
+export const buildSystemPrompt = (userSystemPrompt: string, tools?: MCPTool[]): string => {
  if (tools && tools.length > 0) {
    return SYSTEM_PROMPT.replace('{{ USER_SYSTEM_PROMPT }}', userSystemPrompt)
      .replace('{{ TOOL_USE_EXAMPLES }}', ToolUseExamples)
				`@@ -0,0 +1 @@`
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