cross-llm-mcp

Ein Model Context Protocol (MCP)-Server, der Zugriff auf mehrere Large Language Model (LLM)-APIs bietet, darunter ChatGPT, Claude, Gemini und DeepSeek.

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đŸ€– Cross-LLM MCP Server

Access multiple LLM APIs from one place. Call ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, Mistral, and Hugging Face Inference Router with intelligent model selection, preferences, and prompt logging.

An MCP (Model Context Protocol) server that provides unified access to multiple Large Language Model APIs for AI coding environments like Cursor and Claude Desktop.

Trust Score

Why Use Cross-LLM MCP?

  • 🌐 9 LLM Providers – ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, Mistral, Hugging Face
  • 🎯 Smart Model Selection – Tag-based preferences (coding, business, reasoning, math, creative, general)
  • 📊 Prompt Logging – Track all prompts with history, statistics, and analytics
  • 💰 Cost Optimization – Choose flagship or cheaper models based on preference
  • ⚡ Easy Setup – One-click install in Cursor or simple manual setup
  • 🔄 Call All LLMs – Get responses from all providers simultaneously

Quick Start

Ready to access multiple LLMs? Install in seconds:

Install in Cursor (Recommended):

🔗 Install in Cursor

Or install manually:

npm install -g cross-llm-mcp
# Or from source:
git clone https://github.com/JamesANZ/cross-llm-mcp.git
cd cross-llm-mcp && npm install && npm run build

Features

đŸ€– Individual LLM Tools

  • call-chatgpt – OpenAI's ChatGPT API
  • call-claude – Anthropic's Claude API
  • call-deepseek – DeepSeek API
  • call-gemini – Google's Gemini API
  • call-grok – xAI's Grok API
  • call-kimi – Moonshot AI's Kimi API
  • call-perplexity – Perplexity AI API
  • call-mistral – Mistral AI API
  • call-huggingface – Hugging Face Inference Router (OpenAI-compatible Hub models)

🔄 Combined Tools

  • call-all-llms – Call all LLMs with the same prompt
  • call-llm – Call a specific provider by name

⚙ Preferences & Model Selection

  • get-user-preferences – Get current preferences
  • set-user-preferences – Set default model, cost preference, and tag-based preferences
  • get-models-by-tag – Find models by tag (coding, business, reasoning, math, creative, general)

📝 Prompt Logging

  • get-prompt-history – View prompt history with filters
  • get-prompt-stats – Get statistics about prompt logs
  • delete-prompt-entries – Delete log entries by criteria
  • clear-prompt-history – Clear all prompt logs

Installation

Cursor (One-Click)

Click the install link above or use:

cursor://anysphere.cursor-deeplink/mcp/install?name=cross-llm-mcp&config=eyJjcm9zcy1sbG0tbWNwIjp7ImNvbW1hbmQiOiJucHgiLCJhcmdzIjpbIi15IiwiY3Jvc3MtbGxtLW1jcCJdfX0=

After installation, add your API keys in Cursor settings (see Configuration below).

Manual Installation

Requirements: Node.js 18+ and npm

# Clone and build
git clone https://github.com/JamesANZ/cross-llm-mcp.git
cd cross-llm-mcp
npm install
npm run build

Claude Desktop

Add to claude_desktop_config.json:

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

{
  "mcpServers": {
    "cross-llm-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/cross-llm-mcp/build/index.js"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here",
        "ANTHROPIC_API_KEY": "your_anthropic_api_key_here",
        "DEEPSEEK_API_KEY": "your_deepseek_api_key_here",
        "GEMINI_API_KEY": "your_gemini_api_key_here",
        "XAI_API_KEY": "your_grok_api_key_here",
        "KIMI_API_KEY": "your_kimi_api_key_here",
        "PERPLEXITY_API_KEY": "your_perplexity_api_key_here",
        "MISTRAL_API_KEY": "your_mistral_api_key_here",
        "HF_TOKEN": "your_huggingface_token_here"
      }
    }
  }
}

Restart Claude Desktop after configuration.

Configuration

API Keys

Set environment variables for the LLM providers you want to use:

export OPENAI_API_KEY="your_openai_api_key"
export ANTHROPIC_API_KEY="your_anthropic_api_key"
export DEEPSEEK_API_KEY="your_deepseek_api_key"
export GEMINI_API_KEY="your_gemini_api_key"
export XAI_API_KEY="your_grok_api_key"
export KIMI_API_KEY="your_kimi_api_key"
export PERPLEXITY_API_KEY="your_perplexity_api_key"
export MISTRAL_API_KEY="your_mistral_api_key"
export HF_TOKEN="your_huggingface_token"
# Or: HUGGINGFACE_API_KEY (same as HF_TOKEN)
# Optional: DEFAULT_HUGGINGFACE_MODEL, HUGGINGFACE_INFERENCE_BASE_URL (default https://router.huggingface.co/v1)

Getting API Keys

Running Hub models locally (outside this MCP)

This server calls Hugging Face’s hosted Inference Router; it does not download weights or run PyTorch/GGUF inside Node. To run models on your machine, use tools such as Ollama, llama.cpp, Text Generation Inference, or Hugging Face Inference Endpoints, then point other clients at those services if they expose an API.

Usage Examples

Call ChatGPT

Get a response from OpenAI:

{
  "tool": "call-chatgpt",
  "arguments": {
    "prompt": "Explain quantum computing in simple terms",
    "temperature": 0.7,
    "max_tokens": 500
  }
}

Call Hugging Face

Get a response from a Hub model via the Inference Router (model is the Hub repo id, e.g. Qwen/Qwen2.5-7B-Instruct):

{
  "tool": "call-huggingface",
  "arguments": {
    "prompt": "Reply with exactly: ok",
    "model": "Qwen/Qwen2.5-7B-Instruct",
    "temperature": 0.3,
    "max_tokens": 32
  }
}

Call All LLMs

Get responses from all providers:

{
  "tool": "call-all-llms",
  "arguments": {
    "prompt": "Write a short poem about AI",
    "temperature": 0.8
  }
}

Set Tag-Based Preferences

Automatically use the best model for each task type:

{
  "tool": "set-user-preferences",
  "arguments": {
    "defaultModel": "gpt-4o",
    "costPreference": "cheaper",
    "tagPreferences": {
      "coding": "deepseek-r1",
      "general": "gpt-4o",
      "business": "claude-3.5-sonnet-20241022",
      "reasoning": "deepseek-r1",
      "math": "deepseek-r1",
      "creative": "gpt-4o"
    }
  }
}

Get Prompt History

View your prompt logs:

{
  "tool": "get-prompt-history",
  "arguments": {
    "provider": "chatgpt",
    "limit": 10
  }
}

Model Tags

Models are tagged by their strengths:

  • coding: deepseek-r1, deepseek-coder, gpt-4o, claude-3.5-sonnet-20241022
  • business: claude-3-opus-20240229, gpt-4o, gemini-1.5-pro
  • reasoning: deepseek-r1, o1-preview, claude-3.5-sonnet-20241022
  • math: deepseek-r1, o1-preview, o1-mini
  • creative: gpt-4o, claude-3-opus-20240229, gemini-1.5-pro
  • general: gpt-4o-mini, claude-3-haiku-20240307, gemini-1.5-flash

Use Cases

  • Multi-Perspective Analysis – Get different perspectives from multiple LLMs
  • Model Comparison – Compare responses to understand strengths and weaknesses
  • Cost Optimization – Choose the most cost-effective model for each task
  • Quality Assurance – Cross-reference responses from multiple models
  • Intelligent Selection – Automatically use the best model for coding, business, reasoning, etc.
  • Prompt Analytics – Track usage, costs, and patterns with automatic logging

Technical Details

Built with: Node.js, TypeScript, MCP SDK
Dependencies: @modelcontextprotocol/sdk, superagent, zod
Platforms: macOS, Windows, Linux

Preference Storage:

  • Unix/macOS: ~/.cross-llm-mcp/preferences.json
  • Windows: %APPDATA%/cross-llm-mcp/preferences.json

Prompt Log Storage:

  • Unix/macOS: ~/.cross-llm-mcp/prompts.json
  • Windows: %APPDATA%/cross-llm-mcp/prompts.json

Contributing

⭐ If this project helps you, please star it on GitHub! ⭐

Contributions welcome! Please open an issue or submit a pull request.

License

MIT License – see LICENSE.md for details.

Support

If you find this project useful, consider supporting it:

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