What is Llm Bridge Mcp

llm-bridge-mcp is a model-agnostic Message Control Protocol (MCP) server that facilitates seamless integration with various Large Language Models (LLMs) such as GPT, DeepSeek, and Claude. It provides a standardized interface for AI agents to interact with multiple LLMs.

Use cases

Use cases include developing chatbots that can switch between different LLMs, creating applications that require diverse language capabilities, and conducting research that involves multiple AI models.

How to use

To use llm-bridge-mcp, clone the repository, install the required dependencies, and configure your API keys in a .env file. You can then add the server entry to your application configuration and run it using the specified command.

Key features

Key features include a unified interface to multiple LLM providers, type safety and validation with Pydantic AI, customizable parameters like temperature and max tokens, and usage tracking and metrics.

Where to use

llm-bridge-mcp can be used in various fields such as AI development, natural language processing, chatbots, and any application requiring interaction with large language models.

LLM Bridge MCP

LLM Bridge MCP allows AI agents to interact with multiple large language models through a standardized interface. It leverages the Message Control Protocol (MCP) to provide seamless access to different LLM providers, making it easy to switch between models or use multiple models in the same application.

Features

• Unified interface to multiple LLM providers:
  • OpenAI (GPT models)
  • Anthropic (Claude models)
  • Google (Gemini models)
  • DeepSeek
  • …
• Built with Pydantic AI for type safety and validation
• Supports customizable parameters like temperature and max tokens
• Provides usage tracking and metrics

Tools

The server implements the following tool:

run_llm(
    prompt: str,
    model_name: KnownModelName = "openai:gpt-4o-mini",
    temperature: float = 0.7,
    max_tokens: int = 8192,
    system_prompt: str = "",
) -> LLMResponse

• prompt: The text prompt to send to the LLM
• model_name: Specific model to use (default: “openai:gpt-4o-mini”)
• temperature: Controls randomness (0.0 to 1.0)
• max_tokens: Maximum number of tokens to generate
• system_prompt: Optional system prompt to guide the model’s behavior

Installation

Installing via Smithery

To install llm-bridge-mcp for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @sjquant/llm-bridge-mcp --client claude

Manual Installation

1. Clone the repository:

git clone https://github.com/yourusername/llm-bridge-mcp.git
cd llm-bridge-mcp

2. Install uv (if not already installed):

# On macOS
brew install uv

# On Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Configuration

Create a .env file in the root directory with your API keys:

OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
GOOGLE_API_KEY=your_google_api_key
DEEPSEEK_API_KEY=your_deepseek_api_key

Usage

Using with Claude Desktop or Cursor

Add a server entry to your Claude Desktop configuration file or .cursor/mcp.json:

Troubleshooting

Common Issues

1. “spawn uvx ENOENT” Error

This error occurs when the system cannot find the uvx executable in your PATH. To resolve this:

Solution: Use the full path to uvx

Find the full path to your uvx executable:

# On macOS/Linux
which uvx

# On Windows
where.exe uvx

Then update your MCP server configuration to use the full path:

License

This project is licensed under the MIT License - see the LICENSE file for details.