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MCP LLM Bridge: Seamless Integration & Instant AI Power - MCP Implementation

MCP LLM Bridge: Seamless Integration & Instant AI Power

MCP LLM Bridge: Seamlessly connect Ollama models to your MCP server via fetch URLs—effortless setup, instant AI power for developers and teams.

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Ranked in the top 3% of all AI tools in its category

About MCP LLM Bridge

What is MCP LLM Bridge: Seamless Integration & Instant AI Power?

MCP LLM Bridge acts as a versatile connector between the Model Context Protocol (MCP) ecosystem and OpenAI-compatible LLMs like Ollama. Think of it as the Swiss Army knife for AI workflows—enabling frictionless communication between your MCP servers and any language model adhering to OpenAI’s API standards. Whether you’re prototyping with local models or scaling production systems, this bridge simplifies the heavy lifting so you can focus on building.

How to Use MCP LLM Bridge: A Practical Playbook

Let’s break down the setup in three actionable steps:

  1. Install dependencies: Use the Astral installer and Git to grab the repo essentials.
  2. Configure your stack: Tweak the main.py file to point to your MCP server directory and set up LLM endpoints (like Ollama’s localhost).
  3. Activate & run: Source your virtual environment and launch the bridge—voilà, instant AI connectivity!
# Example config snippet
llm_config=LLMConfig(
    api_key="ollama", 
    model="llama3.2",
    base_url="http://localhost:11434/v1"
)

MCP LLM Bridge Features

Key Features: Where the Magic Happens

  • Plug-and-play compatibility: Works with any OpenAI API-compliant endpoint, from Ollama to your custom setups
  • Local-first focus: No cloud dependency—run models directly from your machine
  • Configurable to the max: Swap models, adjust sampling parameters, or tweak server paths with minimal code changes
  • Future-proof design: Built to integrate with Anthropic’s expanding MCP toolset

Use Cases: Beyond the Obvious

This bridge isn’t just for devops—here’s where it truly shines:

  • Rapid prototyping: Test new models locally before production deployment
  • Private AI playgrounds: Keep sensitive workflows offline with self-hosted models
  • Multi-model experimentation: Compare performance across LLMs without reconfiguring pipelines
  • Serverless workflows: Seamlessly integrate with existing MCP tools like the Fetch server

MCP LLM Bridge FAQ

FAQ: Gotchas & Got Your Back

  • Do I need an API key? Only if using cloud-based LLMs—local setups like Ollama can skip this step
  • Can I use custom models? Absolutely! Just ensure they expose an OpenAI-style API endpoint
  • Where’s the error log? Check the terminal output during execution for real-time diagnostics
  • How do I contribute? Submit PRs to the GitHub repo—we ❤️ improvements!

Content

MCP LLM Bridge

A bridge connecting Model Context Protocol (MCP) servers to OpenAI-compatible LLMs like Ollama Read more about MCP by Anthropic here:

Quick Start

# 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 .


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
 mcp_server_params=StdioServerParameters(
            command="uv",
            # CHANGE THIS = it needs to be an absolute directory! add the mcp fetch server at the directory (clone from https://github.com/modelcontextprotocol/servers/)
            args=["--directory", "~/llms/mcp/mc-server-fetch/servers/src/fetch", "run", "mcp-server-fetch"],
            env=None
        ),
        # llm_config=LLMConfig(
        #     api_key=os.getenv("OPENAI_API_KEY"),
        #     model=os.getenv("OPENAI_MODEL", "gpt-4o"),
        #     base_url=None
        # ),
        llm_config=LLMConfig(
            api_key="ollama",  # Can be any string for local testing
            model="llama3.2",
            base_url="http://localhost:11434/v1"  # Point to your local model's endpoint
        ),
)

Additional Endpoint Support

The bridge also works with any endpoint implementing the OpenAI API specification:

Ollama

llm_config=LLMConfig(
    api_key="not-needed",
    model="mistral-nemo:12b-instruct-2407-q8_0",
    base_url="http://localhost:11434/v1"
)

License

MIT

Contributing

PRs welcome.

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