LLM Bridge MCP server for AI agents

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
- And more
Built with Pydantic AI for type safety and validation
Supports customizable parameters like temperature and max tokens
Provides usage tracking and metrics

Available 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

Parameters

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

The easiest way to install llm-bridge-mcp for Claude Desktop is 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

"spawn uvx ENOENT" Error

This error occurs when the system cannot find the uvx executable in your PATH.

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
    }
  }
}

How to install this MCP server

For Claude Code

To add this MCP server to Claude Code, run this command in your terminal:

claude mcp add-json "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"}}'

See the official Claude Code MCP documentation for more details.

For Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > Tools & Integrations and click "New MCP Server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "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"
            }
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.

For Claude Desktop

To add this MCP server to Claude Desktop:

1. Find your configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

2. Add this to your configuration file:

{
    "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"
            }
        }
    }
}

3. Restart Claude Desktop for the changes to take effect