feat(mcp): hide ask_codebase tool when no LLM providers configured

- Modified web MCP server to check configured language models before
  registering the ask_codebase tool
- Made createMcpServer() async to support awaiting the config check
- Updated standalone MCP package to check /api/models before registering
  ask_codebase tool
- Tool is now only visible when at least one language model is configured

Co-authored-by: Michael Sukkarieh <msukkari@users.noreply.github.com>

Author: cursoragent

packages/mcp/src/index.ts

@@ -14,14 +14,8 @@ import { buildTreeNodeIndex, joinTreePath, normalizeTreePath, sortTreeEntries }
 
 const dedent = _dedent.withOptions({ alignValues: true });
 
-// Create MCP server
-const server = new McpServer({
-    name: 'sourcebot-mcp-server',
-    version: '0.1.0',
-});
-
-
-server.tool(
+function registerTools(server: McpServer, hasLanguageModels: boolean) {
+    server.tool(
     "search_code",
     dedent`
     Searches for code that matches the provided search query as a substring by default, or as a regular expression if useRegex is true. Useful for exploring remote repositories by searching for exact symbols, functions, variables, or specific code patterns. To determine if a repository is indexed, use the \`list_repos\` tool. By default, searches are global and will search the default branch of all repositories. Searches can be scoped to specific repositories, languages, and branches. When referencing code outputted by this tool, always include the file's external URL as a link. This makes it easier for the user to view the file, even if they don't have it locally checked out.
@@ -413,46 +407,58 @@ server.tool(
     }
 );
 
-server.tool(
-    "ask_codebase",
-    dedent`
-    Ask a natural language question about the codebase. This tool uses an AI agent to autonomously search code, read files, and find symbol references/definitions to answer your question.
+    if (hasLanguageModels) {
+        server.tool(
+            "ask_codebase",
+            dedent`
+            Ask a natural language question about the codebase. This tool uses an AI agent to autonomously search code, read files, and find symbol references/definitions to answer your question.
 
-    The agent will:
-    - Analyze your question and determine what context it needs
-    - Search the codebase using multiple strategies (code search, symbol lookup, file reading)
-    - Synthesize findings into a comprehensive answer with code references
+            The agent will:
+            - Analyze your question and determine what context it needs
+            - Search the codebase using multiple strategies (code search, symbol lookup, file reading)
+            - Synthesize findings into a comprehensive answer with code references
 
-    Returns a detailed answer in markdown format with code references, plus a link to view the full research session (including all tool calls and reasoning) in the Sourcebot web UI.
+            Returns a detailed answer in markdown format with code references, plus a link to view the full research session (including all tool calls and reasoning) in the Sourcebot web UI.
 
-    When using this in shared environments (e.g., Slack), you can set the visibility parameter to 'PUBLIC' to ensure everyone can access the chat link.
+            When using this in shared environments (e.g., Slack), you can set the visibility parameter to 'PUBLIC' to ensure everyone can access the chat link.
 
-    This is a blocking operation that may take 30-60+ seconds for complex questions as the agent researches the codebase.
-    `,
-    askCodebaseRequestSchema.shape,
-    { readOnlyHint: true },
-    async (request: AskCodebaseRequest) => {
-        const response = await askCodebase(request);
+            This is a blocking operation that may take 30-60+ seconds for complex questions as the agent researches the codebase.
+            `,
+            askCodebaseRequestSchema.shape,
+            { readOnlyHint: true },
+            async (request: AskCodebaseRequest) => {
+                const response = await askCodebase(request);
 
-        // Format the response with the answer and a link to the chat
-        const formattedResponse = dedent`
-        ${response.answer}
+                // Format the response with the answer and a link to the chat
+                const formattedResponse = dedent`
+                ${response.answer}
 
-        ---
-        **View full research session:** ${response.chatUrl}
-        **Model used:** ${response.languageModel.model}
-        `;
+                ---
+                **View full research session:** ${response.chatUrl}
+                **Model used:** ${response.languageModel.model}
+                `;
 
-        return {
-            content: [{
-                type: "text",
-                text: formattedResponse,
-            }],
-        };
+                return {
+                    content: [{
+                        type: "text",
+                        text: formattedResponse,
+                    }],
+                };
+            }
+        );
     }
-);
+}
 
 const runServer = async () => {
+    const server = new McpServer({
+        name: 'sourcebot-mcp-server',
+        version: '0.1.0',
+    });
+
+    const models = await listLanguageModels();
+    const hasLanguageModels = models.length > 0;
+    registerTools(server, hasLanguageModels);
+
     const transport = new StdioServerTransport();
     await server.connect(transport);
 }

packages/web/src/app/api/(server)/mcp/route.ts

@@ -79,7 +79,7 @@ export const POST = apiHandler(async (request: NextRequest) => {
                 },
             });
 
-            const mcpServer = createMcpServer();
+            const mcpServer = await createMcpServer();
             await mcpServer.connect(transport);
 
             return transport.handleRequest(request);

packages/web/src/features/mcp/server.ts

@@ -1,5 +1,5 @@
 import { listRepos } from '@/app/api/(server)/repos/listReposApi';
-import { getConfiguredLanguageModelsInfo } from "../chat/utils.server";
+import { getConfiguredLanguageModels, getConfiguredLanguageModelsInfo } from "../chat/utils.server";
 import { askCodebase } from '@/features/mcp/askCodebase';
 import {
     languageModelInfoSchema,
@@ -66,12 +66,15 @@ const TOOL_DESCRIPTIONS = {
         `,
 };
 
-export function createMcpServer(): McpServer {
+export async function createMcpServer(): Promise<McpServer> {
     const server = new McpServer({
         name: 'sourcebot-mcp-server',
         version: SOURCEBOT_VERSION,
     });
 
+    const configuredModels = await getConfiguredLanguageModels();
+    const hasLanguageModels = configuredModels.length > 0;
+
     server.registerTool(
         "search_code",
         {
@@ -493,43 +496,45 @@ export function createMcpServer(): McpServer {
         }
     );
 
-    server.registerTool(
-        "ask_codebase",
-        {
-            description: TOOL_DESCRIPTIONS.ask_codebase,
-            annotations: { readOnlyHint: true },
-            inputSchema: z.object({
-                query: z.string().describe("The query to ask about the codebase."),
-                repos: z.array(z.string()).optional().describe("The repositories accessible to the agent. If not provided, all repositories are accessible."),
-                languageModel: languageModelInfoSchema.optional().describe("The language model to use. If not provided, defaults to the first model in the config."),
-                visibility: z.enum(['PRIVATE', 'PUBLIC']).optional().describe("The visibility of the chat session. Defaults to PRIVATE for authenticated users."),
-            }),
-        },
-        async (request) => {
-            const result = await askCodebase({
-                query: request.query,
-                repos: request.repos,
-                languageModel: request.languageModel,
-                visibility: request.visibility as ChatVisibility | undefined,
-                source: 'mcp',
-            });
+    if (hasLanguageModels) {
+        server.registerTool(
+            "ask_codebase",
+            {
+                description: TOOL_DESCRIPTIONS.ask_codebase,
+                annotations: { readOnlyHint: true },
+                inputSchema: z.object({
+                    query: z.string().describe("The query to ask about the codebase."),
+                    repos: z.array(z.string()).optional().describe("The repositories accessible to the agent. If not provided, all repositories are accessible."),
+                    languageModel: languageModelInfoSchema.optional().describe("The language model to use. If not provided, defaults to the first model in the config."),
+                    visibility: z.enum(['PRIVATE', 'PUBLIC']).optional().describe("The visibility of the chat session. Defaults to PRIVATE for authenticated users."),
+                }),
+            },
+            async (request) => {
+                const result = await askCodebase({
+                    query: request.query,
+                    repos: request.repos,
+                    languageModel: request.languageModel,
+                    visibility: request.visibility as ChatVisibility | undefined,
+                    source: 'mcp',
+                });
 
-            if (isServiceError(result)) {
-                return {
-                    content: [{ type: "text", text: `Failed to ask codebase: ${result.message}` }],
-                };
-            }
+                if (isServiceError(result)) {
+                    return {
+                        content: [{ type: "text", text: `Failed to ask codebase: ${result.message}` }],
+                    };
+                }
 
-            const formattedResponse = dedent`
-            ${result.answer}
+                const formattedResponse = dedent`
+                ${result.answer}
 
-            ---
-            **View full research session:** ${result.chatUrl}
-            **Model used:** ${result.languageModel.model}
-            `;
-            return { content: [{ type: "text", text: formattedResponse }] };
-        }
-    );
+                ---
+                **View full research session:** ${result.chatUrl}
+                **Model used:** ${result.languageModel.model}
+                `;
+                return { content: [{ type: "text", text: formattedResponse }] };
+            }
+        );
+    }
 
     return server;
 }