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MCP LLM Bridge

@virajsharma2000

Repository

Basic information

Category

AI & Agents

License

MIT license

Transports

stdio

Author

virajsharma2000

About MCP LLM Bridge

A Simple bridge from Ollama to a fetch url mcp server

MCP LLM Bridge config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "simple-mcp-ollama-bridge": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

MCP LLM Bridge tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

Frequently asked questions

1

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.

2

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.

3

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.

4

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`.

5

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.

Comments

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