mcp-llm-bridge

# MCP LLM Bridge A bridge connecting Model Context Protocol (MCP) servers to OpenAI-compatible LLMs. Primary support for OpenAI API, with additional compatibility for local endpoints that implement the OpenAI API specification. The implementation provides a bidirectional protocol translation layer between MCP and OpenAI's function-calling interface. It converts MCP tool specifications into OpenAI function schemas and handles the mapping of function invocations back to MCP tool executions. This enables any OpenAI-compatible language model to leverage MCP-compliant tools through a standardized interface, whether using cloud-based models or local implementations like Ollama. Read more about MCP by Anthropic here: - [Resources](https://modelcontextprotocol.io/docs/concepts/resources) - [Prompts](https://modelcontextprotocol.io/docs/concepts/prompts) - [Tools](https://modelcontextprotocol.io/docs/concepts/tools) - [Sampling](https://modelcontextprotocol.io/docs/concepts/sampling) Demo:  ## Quick Start ```bash # 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 . # Create test database python -m mcp_llm_bridge.create_test_db ``` ## Configuration ### OpenAI (Primary) Create `.env`: ```bash OPENAI_API_KEY=your_key OPENAI_MODEL=gpt-4o # or any other OpenAI model that supports tools ``` 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 config = BridgeConfig( mcp_server_params=StdioServerParameters( command="uvx", args=["mcp-server-sqlite", "--db-path", "test.db"], env=None ), llm_config=LLMConfig( api_key=os.getenv("OPENAI_API_KEY"), model=os.getenv("OPENAI_MODEL", "gpt-4o"), base_url=None ) ) ``` ### Additional Endpoint Support The bridge also works with any endpoint implementing the OpenAI API specification: #### Ollama ```python llm_config=LLMConfig( api_key="not-needed", model="mistral-nemo:12b-instruct-2407-q8_0", base_url="http://localhost:11434/v1" ) ``` Note: After testing various models, including `llama3.2:3b-instruct-fp16`, I found that `mistral-nemo:12b-instruct-2407-q8_0` handles complex queries more effectively. #### LM Studio ```python llm_config=LLMConfig( api_key="not-needed", model="local-model", base_url="http://localhost:1234/v1" ) ``` I didn't test this, but it should work. ## Usage ```bash python -m mcp_llm_bridge.main # Try: "What are the most expensive products in the database?" # Exit with 'quit' or Ctrl+C ``` ## Running Tests Install the package with test dependencies: ```bash uv pip install -e ".[test]" ``` Then run the tests: ```bash python -m pytest -v tests/ ``` ## License [MIT](LICENSE.md) ## Contributing PRs welcome.