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.
@virajsharma2000
基本信息
A Simple bridge from Ollama to a fetch url mcp server
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"simple-mcp-ollama-bridge": {
"command": "uv",
"args": [
"venv"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
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.
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.
main.py)uv for dependency management and installationIt bridges any MCP server to an OpenAI‑compatible LLM, allowing the LLM to use MCP resources, prompts, and tools.
Python, `uv`, and an LLM endpoint that implements the OpenAI API (e.g., Ollama). No additional runtime beyond the Python environment.
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.
Yes, the bridge can connect to any OpenAI‑compatible API, including GPT‑4o, by setting the appropriate `api_key` and `model`.
The MCP server communicates over stdio (via `StdioServerParameters`), while the LLM is accessed via HTTP using the OpenAI API format.

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