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OpenAI SDK Knowledge MCP Server

by seratch
README.md4 kB
# OpenAI SDK Knowledge MCP Server (unofficial) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![TypeScript](https://img.shields.io/badge/TypeScript-007ACC?logo=typescript&logoColor=white)](https://www.typescriptlang.org/) [![Cloudflare Workers](https://img.shields.io/badge/Cloudflare-Workers-F38020?logo=cloudflare&logoColor=white)](https://workers.cloudflare.com/) An MCP server that knows the OpenAI API inside and out. 100% TypeScript built with [OpenAI Agents SDK](https://openai.github.io/openai-agents-js/), [Hono](https://hono.dev), Cloudflare Workers, and [Drizzle ORM](https://orm.drizzle.team/). Powered by RAG and ready to answer your technical questions. <img width="600" alt="website" src="./public/images/readme_website.png" /> ## Developer Highlights - **Cloudflare stack**: Fully leverages Cloudflare Workers, Queues, D1, Vectorize, and AI Gateway. - **Streamable HTTP MPC Server**: Compatible with any MPC clients. - **ChatGPT Deep Research connector**: Meets ChatGPT's Deep Research connector requirements. - **Always updated**: Continuously fetches OpenAI repos and community forums for new content. - **Rapidly built with AI**: Developed hand in hand with various AI coding tools. ## Streamable HTTP MCP Server Use a publicly accessible URL (e.g., ngrok, Cloudflare Tunnel) to serve the endpoints for MCP clients. You can generate the token on the top page: ```json { "mcpServers": { "openai-sdk-knowledge.org": { "type": "streamable-http", "url": "https://openai-sdk-knowledge.org/mcp", "headers": { "Authorization": "Bearer {your api key here}" } } } } ``` For example, you can add this MCP server to Cursor: <img width="600" alt="Cursor MCP server" src="./public/images/readme_cursor_mcp.png" /> Not only Cursor—you can use this MCP server with any other tools supporting MCP server connections. ### OpenAI Responses API's Hosted MCP Server Tool You can pass `https://openai-sdk-knowledge.org/mcp` along with a valid API token: <img width="300" src="./public/images/readme_playground_1.png" /><img width="300" src="./public/images/readme_playground_2.png" /> Then, you can call the tool in the conversation with the Responses API agent: <img width="600" src="./public/images/readme_playground_3.png" /> ### ChatGPT Deep Research MCP Connector Also, for ChatGPT Deep Research customer connector, use the same URL. When the ChatGPT server accesses this app's MCP server endpoint, it returns `search` and `fetch` tools as well (see [the documentation](https://platform.openai.com/docs/mcp) for details). <img width="600" alt="Use the connector in Deep Research" src="./public/images/readme_chatgpt_deep_research.png" /> ## Run Locally ```bash # Clone and install git clone https://github.com/seratch/openai-sdk-knowledge-org.git cd openai-sdk-knowledge-org/ npm install # Configure (add your OpenAI API key) cp .dev.vars.example .dev.vars # Edit .dev.vars: OPENAI_API_KEY=sk-your-key-here # Run it npm run dev ``` You can access `http://localhost:8787` and see how it works. **Requirements**: Node.js 22+ and API keys (OpenAI, GitHub) ## Architecture This app is essentially a simple web app that runs on Cloudflare Workers. The web app provides MCP server protocol compatible endpoints, as well as a web user interface. For the RAG data pipeline, it collects data from sources and generates asynchronous tasks to run and enqueue them into Cloudflare’s Queue. ``` src/ ├── agents/ # Internally used agents built with OpenAI Agents SDK ├── pipeline/ # RAG data collection and processing ├── server/mcp/ # MCP protocol implementation ├── server/web/ # Web app implementation ├── storage/ # Vector database (Vectorize) and D1 database access └── index.ts # App entry point ``` <img width="600" src="./public/images/readme_architecture.png" /> ## License MIT

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