MCP LLM Bridge

A bridge connecting Model Context Protocol (MCP) servers to OpenAI-compatible LLMs like Ollama

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Quick Start

# Install
curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/bartolli/mcp-llm-bridge.git
cd mcp-llm-bridge
uv venv
source .venv/bin/activate
uv pip install -e .

Note: reactivate the environment if needed to use the keys in `.env`: `source .venv/bin/activate`

Then configure the bridge in [src/mcp_llm_bridge/main.py](src/mcp_llm_bridge/main.py)

mcp_server_params=StdioServerParameters(

command="uv",

# CHANGE THIS = it needs to be an absolute directory! add the mcp fetch server at the directory (clone from https://github.com/modelcontextprotocol/servers/)

args=["--directory", "~/llms/mcp/mc-server-fetch/servers/src/fetch", "run", "mcp-server-fetch"],

env=None

),

# llm_config=LLMConfig(

# api_key=os.getenv("OPENAI_API_KEY"),

# model=os.getenv("OPENAI_MODEL", "gpt-4o"),

# base_url=None

# ),

llm_config=LLMConfig(

api_key="ollama", # Can be any string for local testing

model="llama3.2",

base_url="http://localhost:11434/v1" # Point to your local model's endpoint

),

)

### Additional Endpoint Support

The bridge also works with any endpoint implementing the OpenAI API specification:

#### Ollama

llm_config=LLMConfig(

api_key="not-needed",

model="mistral-nemo:12b-instruct-2407-q8_0",

base_url="http://localhost:11434/v1"

)

## License

[MIT](LICENSE.md)

## Contributing

PRs welcome.