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LLM Bridge MCP

by sjquant
GitHub
Python
MIT License
3
  • Linux
  • Apple
OverviewInspectNewSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

Integrations

  • Allows access to Google's Gemini models through the same standardized interface used for other LLM providers

  • Provides access to OpenAI's GPT models through a standardized interface, supporting customizable parameters like temperature and max tokens

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
  1. 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 } } }

License

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

This server cannot be installed

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security - not tested
A
license - permissive license
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quality - not tested

Enables AI agents to interact with multiple LLM providers (OpenAI, Anthropic, Google, DeepSeek) through a standardized interface, making it easy to switch between models or use multiple models in the same application.

  1. Features
    1. Tools
      1. Installation
        1. Installing via Smithery
        2. Manual Installation
      2. Configuration
        1. Usage
          1. Using with Claude Desktop or Cursor
          2. Troubleshooting
        2. License

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