
mcp-llm-bridge
MCP implementation that enables communication between MCP servers and OpenAI-compatible LLMs
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The MCP LLM Bridge is a tool that acts as a bridge connecting Model Context Protocol (MCP) servers to OpenAI-compatible LLMs. It provides a bidirectional protocol translation layer between MCP and OpenAI's function-calling interface, enabling any OpenAI-compatible language model to leverage MCP-compliant tools through a standardized interface. The tool supports primary integration with the OpenAI API and offers additional compatibility for local endpoints that implement the OpenAI API specification. Users can configure the tool for different endpoints and models, facilitating the execution of complex queries and tasks using cloud-based or local models like Ollama and LM Studio.
README:
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.
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