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:
Demo:
# 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
Create .env
:
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
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
)
)
The bridge also works with any endpoint implementing the OpenAI API specification:
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.
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.
python -m mcp_llm_bridge.main
# Try: "What are the most expensive products in the database?"
# Exit with 'quit' or Ctrl+C
Install the package with test dependencies:
uv pip install -e ".[test]"
Then run the tests:
python -m pytest -v tests/
PRs welcome.