https://github.com/bartolli/mcp-llm-bridge
MCP implementation that enables communication between MCP servers and OpenAI-compatible LLMs
https://github.com/bartolli/mcp-llm-bridge
Last synced: 11 days ago
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MCP implementation that enables communication between MCP servers and OpenAI-compatible LLMs
- Host: GitHub
- URL: https://github.com/bartolli/mcp-llm-bridge
- Owner: bartolli
- License: mit
- Created: 2024-12-03T02:10:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-28T12:43:51.000Z (about 1 year ago)
- Last Synced: 2025-07-31T19:22:39.015Z (10 months ago)
- Language: Python
- Size: 4.3 MB
- Stars: 337
- Watchers: 10
- Forks: 39
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# 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.