LLM 브리지 MCP

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")
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
    }
  }
}

License

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

Install Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

run_llm

표준화된 인터페이스를 통해 AI 에이전트가 여러 LLM 공급자(OpenAI, Anthropic, Google, DeepSeek)와 상호 작용할 수 있도록 하여 모델 간에 쉽게 전환하거나 동일한 애플리케이션에서 여러 모델을 사용할 수 있습니다.

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LLM Bridge MCP

LLM Bridge MCP

Features

Tools

Installation

Installing via Smithery

Manual Installation

Configuration

Usage

Using with Claude Desktop or Cursor

Troubleshooting

Common Issues

1. "spawn uvx ENOENT" Error

License

Tools

Related MCP Servers

OpenRouter MCP Server

Model Context Provider (MCP) Server

Cross-System Agent Communication MCP Server

MindBridge MCP Server

Appeared in Searches

MCP directory API