🤖 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](https://archestra.ai/mcp-catalog/jamesanz__cross-llm-mcp)

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

🤖 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

OpenAI: https://platform.openai.com/api-keys
Anthropic: https://console.anthropic.com/
DeepSeek: https://platform.deepseek.com/
Google Gemini: https://makersuite.google.com/app/apikey
xAI Grok: https://console.x.ai/
Moonshot AI: https://platform.moonshot.ai/
Perplexity: https://www.perplexity.ai/hub
Mistral: https://console.mistral.ai/
Hugging Face: Create a fine-grained token with Inference (serverless / Inference Providers) access at https://huggingface.co/settings/tokens. See Chat Completion for supported models.

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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