🤖 Cross-LLM MCP Server
Access multiple LLM APIs from one place. Call ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, and Mistral 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.
Why Use Cross-LLM MCP?
- 🌐 8 LLM Providers – ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, Mistral
- 🎯 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):
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 APIcall-claude– Anthropic's Claude APIcall-deepseek– DeepSeek APIcall-gemini– Google's Gemini APIcall-grok– xAI's Grok APIcall-kimi– Moonshot AI's Kimi APIcall-perplexity– Perplexity AI APIcall-mistral– Mistral AI API
🔄 Combined Tools
call-all-llms– Call all LLMs with the same promptcall-llm– Call a specific provider by name
⚙️ Preferences & Model Selection
get-user-preferences– Get current preferencesset-user-preferences– Set default model, cost preference, and tag-based preferencesget-models-by-tag– Find models by tag (coding, business, reasoning, math, creative, general)
📝 Prompt Logging
get-prompt-history– View prompt history with filtersget-prompt-stats– Get statistics about prompt logsdelete-prompt-entries– Delete log entries by criteriaclear-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"
}
}
}
}
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"
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/
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 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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