What is Mcpo

mcpo is a simple and secure proxy server that exposes any MCP tool as an OpenAPI-compatible HTTP server, enabling seamless integration with LLM agents and applications that expect OpenAPI servers.

Use cases

Use cases for mcpo include creating a RESTful API for legacy MCP tools, enabling easy access to multiple MCP functionalities through a single endpoint, and providing documentation for developers to interact with MCP tools effortlessly.

How to use

To use mcpo, you can either run it directly with the command line using ‘uvx’ for fast startup or install it via Python with ‘pip’. For example: ‘uvx mcpo --port 8000 – your_mcp_server_command’. You can also configure multiple MCP tools using a JSON config file.

Key features

Key features of mcpo include instant exposure of MCP tools as OpenAPI servers, no custom protocols or glue code required, support for multiple tools via a single config file, and auto-generated OpenAPI documentation for each tool.

Where to use

mcpo can be used in various fields where integration with OpenAPI is beneficial, such as software development, API management, and machine learning applications that utilize LLM agents.

⚡️ mcpo

Expose any MCP tool as an OpenAPI-compatible HTTP server—instantly.

mcpo is a dead-simple proxy that takes an MCP server command and makes it accessible via standard RESTful OpenAPI, so your tools “just work” with LLM agents and apps expecting OpenAPI servers.

No custom protocol. No glue code. No hassle.

🤔 Why Use mcpo Instead of Native MCP?

MCP servers usually speak over raw stdio, which is:

• 🔓 Inherently insecure
• ❌ Incompatible with most tools
• 🧩 Missing standard features like docs, auth, error handling, etc.

mcpo solves all of that—without extra effort:

• ✅ Works instantly with OpenAPI tools, SDKs, and UIs
• 🛡 Adds security, stability, and scalability using trusted web standards
• 🧠 Auto-generates interactive docs for every tool, no config needed
• 🔌 Uses pure HTTP—no sockets, no glue code, no surprises

What feels like “one more step” is really fewer steps with better outcomes.

mcpo makes your AI tools usable, secure, and interoperable—right now, with zero hassle.

🚀 Quick Usage

We recommend using uv for lightning-fast startup and zero config.

uvx mcpo --port 8000 --api-key "top-secret" -- your_mcp_server_command

Or, if you’re using Python:

pip install mcpo
mcpo --port 8000 --api-key "top-secret" -- your_mcp_server_command

To use an SSE-compatible MCP server, simply specify the server type and endpoint:

mcpo --port 8000 --api-key "top-secret" --server-type "sse" -- http://127.0.0.1:8001/sse

You can also provide headers for the SSE connection:

mcpo --port 8000 --api-key "top-secret" --server-type "sse" --headers '{"Authorization": "Bearer token", "X-Custom-Header": "value"}' -- http://127.0.0.1:8001/sse

To use a Streamable HTTP-compatible MCP server, specify the server type and endpoint:

mcpo --port 8000 --api-key "top-secret" --server-type "streamable_http" -- http://127.0.0.1:8002/mcp

You can also run mcpo via Docker with no installation:

docker run -p 8000:8000 ghcr.io/open-webui/mcpo:main --api-key "top-secret" -- your_mcp_server_command

Example:

uvx mcpo --port 8000 --api-key "top-secret" -- uvx mcp-server-time --local-timezone=America/New_York

That’s it. Your MCP tool is now available at http://localhost:8000 with a generated OpenAPI schema — test it live at http://localhost:8000/docs.

🤝 To integrate with Open WebUI after launching the server, check our docs.

🔄 Using a Config File

You can serve multiple MCP tools via a single config file that follows the Claude Desktop format:

Start via:

mcpo --config /path/to/config.json

Example config.json:

Each tool will be accessible under its own unique route, e.g.:

• http://localhost:8000/memory
• http://localhost:8000/time

Each with a dedicated OpenAPI schema and proxy handler. Access full schema UI at: http://localhost:8000/<tool>/docs (e.g. /memory/docs, /time/docs)

🔧 Requirements

• Python 3.8+
• uv (optional, but highly recommended for performance + packaging)

🛠️ Development & Testing

To contribute or run tests locally:

1. Set up the environment:

   # Clone the repository
   git clone https://github.com/open-webui/mcpo.git
   cd mcpo
   
   # Install dependencies (including dev dependencies)
   uv sync --dev
   

2. Run tests:

   uv run pytest
   

3. Running Locally with Active Changes:

   To run mcpo with your local modifications from a specific branch (e.g., my-feature-branch):

   # Ensure you are on your development branch
   git checkout my-feature-branch
   
   # Make your code changes in the src/mcpo directory or elsewhere
   
   # Run mcpo using uv, which will use your local, modified code
   # This command starts mcpo on port 8000 and proxies your_mcp_server_command
   uv run mcpo --port 8000 -- your_mcp_server_command
   
   # Example with a test MCP server (like mcp-server-time):
   # uv run mcpo --port 8000 -- uvx mcp-server-time --local-timezone=America/New_York
   

   This allows you to test your changes interactively before committing or creating a pull request. Access your locally running mcpo instance at http://localhost:8000 and the auto-generated docs at http://localhost:8000/docs.

🪪 License

MIT

🤝 Contributing

We welcome and strongly encourage contributions from the community!

Whether you’re fixing a bug, adding features, improving documentation, or just sharing ideas—your input is incredibly valuable and helps make mcpo better for everyone.

Getting started is easy:

• Fork the repo
• Create a new branch
• Make your changes
• Open a pull request

Not sure where to start? Feel free to open an issue or ask a question—we’re happy to help you find a good first task.

✨ Star History

---

✨ Let’s build the future of interoperable AI tooling together!