MCP-Bridge

MCP-Bridge acts as a bridge between the OpenAI API and MCP (MCP) tools, allowing developers to leverage MCP tools through the OpenAI API interface.

Overview

MCP-Bridge is designed to facilitate the integration of MCP tools with the OpenAI API. It provides a set of endpoints that can be used to interact with MCP tools in a way that is compatible with the OpenAI API. This allows you to use any client with any MCP tool without explicit support for MCP. For example, see this example of using Open Web UI with the official MCP fetch tool.

Current Features

working features:

• non streaming chat completions with MCP

• streaming chat completions with MCP

• non streaming completions without MCP

• MCP tools

• MCP sampling

• SSE Bridge for external clients

planned features:

• streaming completions are not implemented yet

• MCP resources are planned to be supported

Installation

The recommended way to install MCP-Bridge is to use Docker. See the example compose.yml file for an example of how to set up docker.

Note that this requires an inference engine with tool call support. I have tested this with vLLM with success, though ollama should also be compatible.

Docker installation

1. Clone the repository

2. Edit the compose.yml file

You will need to add a reference to the config.json file in the compose.yml file. Pick any of

• add the config.json file to the same directory as the compose.yml file and use a volume mount (you will need to add the volume manually)
• add a http url to the environment variables to download the config.json file from a url
• add the config json directly as an environment variable

see below for an example of each option:

environment:
  - MCP_BRIDGE__CONFIG__FILE=config.json # mount the config file for this to work
  - MCP_BRIDGE__CONFIG__HTTP_URL=http://10.88.100.170:8888/config.json
  - MCP_BRIDGE__CONFIG__JSON={"inference_server":{"base_url":"http://example.com/v1","api_key":"None"},"mcp_servers":{"fetch":{"command":"uvx","args":["mcp-server-fetch"]}}}

The mount point for using the config file would look like:

    volumes:
      - ./config.json:/mcp_bridge/config.json

3. run the service

docker-compose up --build -d

Manual installation (no docker)

If you want to run the application without docker, you will need to install the requirements and run the application manually.

1. Clone the repository

2. Set up a dependencies:

uv sync

3. Create a config.json file in the root directory

Here is an example config.json file:

{
   "inference_server": {
      "base_url": "http://example.com/v1",
      "api_key": "None"
   },
   "mcp_servers": {
      "fetch": {
        "command": "uvx",
        "args": ["mcp-server-fetch"]
      }
   }
}

4. Run the application:

uv run mcp_bridge/main.py

Usage

Once the application is running, you can interact with it using the OpenAI API.

View the documentation at http://yourserver:8000/docs. There is an endpoint to list all the MCP tools available on the server, which you can use to test the application configuration.

Rest API endpoints

MCP-Bridge exposes many rest api endpoints for interacting with all of the native MCP primatives. This lets you outsource the complexity of dealing with MCP servers to MCP-Bridge without comprimising on functionality. See the openapi docs for examples of how to use this functionality.

SSE Bridge

MCP-Bridge also provides an SSE bridge for external clients. This lets external chat apps with explicit MCP support use MCP-Bridge as a MCP server. Point your client at the SSE endpoint (http://yourserver:8000/mcp-server/sse) and you should be able to see all the MCP tools available on the server.

This also makes it easy to test if your configuration is working correctly. You can use wong2/mcp-cli to test your configuration. npx @wong2/mcp-cli --sse http://localhost:8000/mcp-server/sse

If you want to use the tools inside of claude desktop or other STDIO only MCP clients, you can do this with a tool such as lightconetech/mcp-gateway

Configuration

To add new MCP servers, edit the config.json file.

API Key Authentication

MCP-Bridge supports API key authentication to secure your server. To enable this feature, add an api_key field to your config.json file:

{
    "api_key": "your-secure-api-key-here"
}

When making requests to the MCP-Bridge server, include the API key in the Authorization header as a Bearer token:

Authorization: Bearer your-secure-api-key-here

If the api_key field is empty or not present in the configuration, authentication will be skipped, allowing backward compatibility.

Full Configuration Example

an example config.json file with most of the options explicitly set:

{
    "inference_server": {
        "base_url": "http://localhost:8000/v1",
        "api_key": "None"
    },
    "sampling": {
        "timeout": 10,
        "models": [
            {
                "model": "gpt-4o",
                "intelligence": 0.8,
                "cost": 0.9,
                "speed": 0.3
            },
            {
                "model": "gpt-4o-mini",
                "intelligence": 0.4,
                "cost": 0.1,
                "speed": 0.7
            }
        ]
    },
    "mcp_servers": {
        "fetch": {
            "command": "uvx",
            "args": [
                "mcp-server-fetch"
            ]
        }
    },
    "network": {
        "host": "0.0.0.0",
        "port": 9090
    },
    "logging": {
        "log_level": "DEBUG"
    },
    "api_key": "your-secure-api-key-here"
}

Section	Description
inference_server	The inference server configuration
mcp_servers	The MCP servers configuration
network	uvicorn network configuration
logging	The logging configuration
api_key	API key for server authentication

Support

If you encounter any issues please open an issue or join the discord.

There is also documentation available here.

How does it work

The application sits between the OpenAI API and the inference engine. An incoming request is modified to include tool definitions for all MCP tools available on the MCP servers. The request is then forwarded to the inference engine, which uses the tool definitions to create tool calls. MCP bridge then manage the calls to the tools. The request is then modified to include the tool call results, and is returned to the inference engine again so the LLM can create a response. Finally, the response is returned to the OpenAI API.

sequenceDiagram
    participant OpenWebUI as Open Web UI
    participant MCPProxy as MCP Proxy
    participant MCPserver as MCP Server
    participant InferenceEngine as Inference Engine

    OpenWebUI ->> MCPProxy: Request
    MCPProxy ->> MCPserver: list tools
    MCPserver ->> MCPProxy: list of tools
    MCPProxy ->> InferenceEngine: Forward Request
    InferenceEngine ->> MCPProxy: Response
    MCPProxy ->> MCPserver: call tool
    MCPserver ->> MCPProxy: tool response
    MCPProxy ->> InferenceEngine: llm uses tool response
    InferenceEngine ->> MCPProxy: Response
    MCPProxy ->> OpenWebUI: Return Response

Contribution Guidelines

Contributions to MCP-Bridge are welcome! To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Create a pull request to the main repository.

License

MCP-Bridge is licensed under the MIT License. See the LICENSE file for more information.