MCP LLM Bridge

What is MCP LLM Bridge?

MCP LLM Bridge connects Model Context Protocol (MCP) servers to OpenAI‑compatible LLMs such as Ollama. It is intended for developers who want to use local or remote LLMs with MCP‑based tooling.

How to use MCP LLM Bridge?

Clone the repository, set up a Python virtual environment with uv, install the package, then edit src/mcp_llm_bridge/main.py to configure the MCP server parameters and LLM connection (API key, model, base URL). The bridge supports any endpoint implementing the OpenAI API specification, including Ollama running locally.

Key features of MCP LLM Bridge

• Connects MCP servers to OpenAI‑compatible LLMs
• Works with any OpenAI‑compliant endpoint (e.g., Ollama, GPT‑4o)
• Configured via a single Python file (main.py)
• Supports local LLM models for privacy and offline use
• Uses uv for dependency management and installation

Use cases of MCP LLM Bridge

• Run MCP‑enabled tools with a local Ollama model instead of a cloud API
• Bridge existing MCP servers to OpenAI‑compatible LLMs for sampling or tool use
• Test MCP integrations against a local LLM before deploying to a production endpoint

FAQ from MCP LLM Bridge

What does MCP LLM Bridge do?

It bridges any MCP server to an OpenAI‑compatible LLM, allowing the LLM to use MCP resources, prompts, and tools.

What dependencies are required?

Python, uv, and an LLM endpoint that implements the OpenAI API (e.g., Ollama). No additional runtime beyond the Python environment.

How do I configure the bridge?

Edit src/mcp_llm_bridge/main.py to set mcp_server_params (pointing to your MCP server) and llm_config (API key, model, base URL). Example configurations for Ollama and GPT‑4o are shown in the file.

Does it work with cloud LLMs?

Yes, the bridge can connect to any OpenAI‑compatible API, including GPT‑4o, by setting the appropriate api_key and model.

What is the transport mechanism?

The MCP server communicates over stdio (via StdioServerParameters), while the LLM is accessed via HTTP using the OpenAI API format.