LLM Bridge MCP

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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

Related MCP server: Model Context Provider (MCP) Server

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

Clone the repository:

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

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:

"mcpServers": { "llm-bridge": { "command": "uvx", "args": [ "llm-bridge-mcp" ], "env": { "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" } } }

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:

"mcpServers": { "llm-bridge": { "command": "/full/path/to/uvx", // Replace with your actual path "args": [ "llm-bridge-mcp" ], "env": { // ... your environment variables } } }