FileSearch MCP Server

A minimal FastMCP 2.x server in TypeScript that exposes one RPC—retrieveDocs(question)—which returns raw ranked chunks from OpenAI File Search.

Features

• Acts as a simple proxy to OpenAI's /v1/responses endpoint using the file_search tool.
• Reads OPENAI_API_KEY from environment variables.
• Reads vectorStoreId from config.json.
• Uses FastMCP 2.x with addTool and ctx.log.
• Returns raw chunks ({ id, text, score }[]) as a JSON string, extracted from file_search_call.results.
• Includes robustness features: 30s timeout, retry logic (3 attempts) for transient errors (429, 5xx, network), and structured error reporting.
• Optional verbose logging via DEBUG_OPENAI environment variable.

Prerequisites

• Node.js (v18 or later recommended)
• npm (or pnpm/yarn)
• An OpenAI API Key
• An existing OpenAI Vector Store ID
• Git (for cloning)

Setup

1. Clone the repository (if applicable):

   # git clone <repository-url>
   # cd filesearch-mcp-server
   

2. Install dependencies: Installs required packages.

   npm install
   

3. Configure:

   • config.json: Copy config.template.json to config.json and add your Vector Store ID.

     cp config.template.json config.json
     

     Then edit config.json:

     {
       "vectorStoreId": "vs_YOUR_VECTOR_STORE_ID"
     }
     

     (Replace vs_YOUR_VECTOR_STORE_ID with your actual ID. This file is ignored by git via .gitignore.)

   • Environment Variables: Create a .env file (or set environment variables directly). You can copy the example:

     cp .env.example .env
     

     Then edit .env with your API key:

     # Required: Your OpenAI API Key
     OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
     
     # Optional: Set to "true" or "1" for verbose OpenAI API response logging
     # DEBUG_OPENAI=false
     

     (This file is ignored by git via .gitignore.)

     Alternatively, configure via Cline MCP Settings (cline_mcp_setting.json):

     {
       "servers": [
         {
           "id": "filesearch-mcp-server",
           "command": "npm start",
           "working_directory": "path/to/filesearch-mcp-server",
           "environment": {
             "OPENAI_API_KEY": "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
             "DEBUG_OPENAI": "false"
           }
         }
       ]
     }
     

4. Build the TypeScript code:

   npm run build
   

Running the Server

npm start

This command runs node dist/server.js, starting the server using the stdio transport (default). It listens for MCP requests on standard input/output, making it suitable for use with Cline or other MCP clients expecting stdio communication.

Development

• Build: npm run build (Compiles TypeScript to dist/)
• Format: npm run format (Formats code with Prettier)
• Lint: npm run lint (Checks code style with ESLint)
• Watch & Run: npm run dev (Watches for changes, rebuilds, and restarts the server)

Chunk Structure

The server extracts chunks from the output[*].file_search_call.results field in the OpenAI API response. Each chunk in the returned JSON array follows the Chunk interface: { id: string, text: string, score: number }. The id is taken from the result object's id field (with a fallback), text is the chunk content, and score is the relevance score provided by OpenAI.

License

This project is licensed under the MIT License - see the LICENSE file for details.