A Model Context Protocol (MCP) server that provides access to multiple Large Language Model (LLM) APIs including ChatGPT, Claude, and DeepSeek. This allows you to call different LLMs from within any MCP-compatible client and combine their responses.

This MCP server offers five specialized tools for interacting with different LLM providers:

Call OpenAI's ChatGPT API with a prompt.

Input:

• prompt (string): The prompt to send to ChatGPT
• model (optional, string): ChatGPT model to use (default: gpt-4)
• temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
• max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

• ChatGPT response with model information and token usage statistics

Example:

ChatGPT Response
Model: gpt-4

Here's a comprehensive explanation of quantum computing...

---
Usage:
- Prompt tokens: 15
- Completion tokens: 245
- Total tokens: 260

Call Anthropic's Claude API with a prompt.

Input:

• prompt (string): The prompt to send to Claude
• model (optional, string): Claude model to use (default: claude-3-sonnet-20240229)
• temperature (optional, number): Temperature for response randomness (0-1, default: 0.7)
• max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

• Claude response with model information and token usage statistics

Call DeepSeek API with a prompt.

Input:

• prompt (string): The prompt to send to DeepSeek
• model (optional, string): DeepSeek model to use (default: deepseek-chat)
• temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
• max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

• DeepSeek response with model information and token usage statistics

Call all available LLM APIs (ChatGPT, Claude, DeepSeek) with the same prompt and get combined responses.

Input:

• prompt (string): The prompt to send to all LLMs
• temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
• max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

• Combined responses from all LLMs with individual model information and usage statistics
• Summary of successful responses and total tokens used

Example:

Multi-LLM Response

Prompt: Explain quantum computing in simple terms

---

## CHATGPT

Model: gpt-4

Quantum computing is like having a super-powered computer...

---

## CLAUDE

Model: claude-3-sonnet-20240229

Quantum computing represents a fundamental shift...

---

## DEEPSEEK

Model: deepseek-chat

Quantum computing harnesses the principles of quantum mechanics...

---

Summary:
- Successful responses: 3/3
- Total tokens used: 1250

Call a specific LLM provider by name.

Input:

• provider (string): The LLM provider to call ("chatgpt", "claude", or "deepseek")
• prompt (string): The prompt to send to the LLM
• model (optional, string): Model to use (uses provider default if not specified)
• temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
• max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

• Response from the specified LLM with model information and usage statistics

1. Clone this repository:

git clone <repository-url>
cd cross-llm-mcp

2. Install dependencies:

npm install

3. Set up environment variables:

cp env.example .env

4. Edit the .env file with your API keys:

# OpenAI/ChatGPT API Key
OPENAI_API_KEY=your_openai_api_key_here

# Anthropic/Claude API Key
ANTHROPIC_API_KEY=your_anthropic_api_key_here

# DeepSeek API Key
DEEPSEEK_API_KEY=your_deepseek_api_key_here

# Default models for each provider
DEFAULT_CHATGPT_MODEL=gpt-4
DEFAULT_CLAUDE_MODEL=claude-3-sonnet-20240229
DEFAULT_DEEPSEEK_MODEL=deepseek-chat

5. Build the project:

npm run build

1. Visit OpenAI Platform
2. Sign up or log in to your account
3. Create a new API key
4. Add it to your .env file as OPENAI_API_KEY

1. Visit Anthropic Console
2. Sign up or log in to your account
3. Create a new API key
4. Add it to your .env file as ANTHROPIC_API_KEY

1. Visit DeepSeek Platform
2. Sign up or log in to your account
3. Create a new API key
4. Add it to your .env file as DEEPSEEK_API_KEY

Start the MCP server:

npm start

The server runs on stdio and can be connected to any MCP-compatible client.

Here are some example queries you can make with this MCP server:

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

{
  "tool": "call-claude",
  "arguments": {
    "prompt": "What are the benefits of renewable energy?",
    "model": "claude-3-sonnet-20240229"
  }
}

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

{
  "tool": "call-llm",
  "arguments": {
    "provider": "deepseek",
    "prompt": "Explain machine learning algorithms",
    "max_tokens": 800
  }
}

Use call-all-llms to get different perspectives on the same topic from multiple AI models.

Compare responses from different LLMs to understand their strengths and weaknesses.

If one LLM is unavailable, you can still get responses from other providers.

Choose the most cost-effective LLM for your specific use case.

Cross-reference responses from multiple models to validate information.

This MCP server uses the following API endpoints:

• OpenAI: https://api.openai.com/v1/chat/completions
• Anthropic: https://api.anthropic.com/v1/messages
• DeepSeek: https://api.deepseek.com/v1/chat/completions

The server includes comprehensive error handling with detailed messages:

**ChatGPT Error:** OpenAI API key not configured

**Claude Error:** Claude API error: Invalid API key - please check your Anthropic API key

**DeepSeek Error:** DeepSeek API error: Rate limit exceeded - please try again later

**ChatGPT Error:** ChatGPT API error: Payment required - please check your OpenAI billing

**Claude Error:** Claude API error: Network timeout

• OPENAI_API_KEY: Your OpenAI API key
• ANTHROPIC_API_KEY: Your Anthropic API key
• DEEPSEEK_API_KEY: Your DeepSeek API key
• DEFAULT_CHATGPT_MODEL: Default ChatGPT model (default: gpt-4)
• DEFAULT_CLAUDE_MODEL: Default Claude model (default: claude-3-sonnet-20240229)
• DEFAULT_DEEPSEEK_MODEL: Default DeepSeek model (default: deepseek-chat)

• gpt-4
• gpt-4-turbo
• gpt-3.5-turbo
• And other OpenAI models

• claude-3-sonnet-20240229
• claude-3-opus-20240229
• claude-3-haiku-20240307
• And other Anthropic models

• deepseek-chat
• deepseek-coder
• And other DeepSeek models

cross-llm-mcp/
├── src/
│   ├── index.ts          # Main MCP server with all 5 tools
│   ├── types.ts          # TypeScript type definitions
│   └── llm-clients.ts    # LLM API client implementations
├── build/                # Compiled JavaScript output
├── env.example           # Environment variables template
├── example-usage.md      # Detailed usage examples
├── package.json          # Project dependencies and scripts
└── README.md            # This file

• @modelcontextprotocol/sdk - MCP SDK for server implementation
• superagent - HTTP client for API requests
• zod - Schema validation for tool parameters
• dotenv - Environment variable management

npm run build

To add a new LLM provider:

1. Add the provider type to src/types.ts
2. Implement the client in src/llm-clients.ts
3. Add the tool to src/index.ts
4. Update the callAllLLMs method to include the new provider

Server won't start

• Check that all dependencies are installed: npm install
• Verify the build was successful: npm run build
• Ensure the .env file exists and has valid API keys

API errors

• Verify your API keys are correct and active
• Check your API usage limits and billing status
• Ensure you're using supported model names

No responses

• Check that at least one API key is configured
• Verify network connectivity
• Look for error messages in the response

For debugging, you can run the server directly:

node build/index.js

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

Contributions are welcome! Please feel free to submit a Pull Request.

If you encounter any issues or have questions, please:

1. Check the troubleshooting section above
2. Review the error messages for specific guidance
3. Ensure your API keys are properly configured
4. Verify your network connectivity