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MCP LLM Bridge

MCP LLM Bridge

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github.com/virajsharma2000AI & Agents1Updated 85w ago
Repository

About MCP LLM Bridge

MCP LLM Bridge connects MCP servers to OpenAI-compatible LLMs, including Ollama. It is for users who want to work with MCP tools through a local model endpoint.

Key features

  1. 01Connect MCP servers and LLMs
  2. 02Support OpenAI-compatible endpoints
  3. 03Configure MCP server parameters
  4. 04Use Ollama endpoints

Use cases

  1. 01Use MCP tools with Ollama
  2. 02Connect an MCP server to an OpenAI-compatible model endpoint

MCP support

The bridge connects MCP servers to OpenAI-compatible LLMs using configured MCP server parameters. The README demonstrates this with an MCP fetch server.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP LLM Bridge

A bridge connecting Model Context Protocol (MCP) servers to OpenAI-compatible LLMs like Ollama Read more about MCP by Anthropic here:

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)

```python
 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

Contributing

PRs welcome.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.