alvarordgs/mcp_server

Crypto MCP Server

A Model Context Protocol (MCP) server that provides cryptocurrency analysis tools using CoinGecko API data and AI-powered insights.

Features

• Real-time Crypto Data: Get current prices, market cap, volume, and 24h changes
• AI-Powered Analysis: Comprehensive cryptocurrency analysis with risk assessment
• Multi-Coin Support: Analyze multiple cryptocurrencies simultaneously
• MCP Protocol: Standardized interface for AI assistants and tools

Available Tools

1. get_crypto_price

Get current price and market data for a specific cryptocurrency.

Parameters:

• coin_id (string): The coin ID (e.g., 'bitcoin', 'ethereum', 'cardano')

Example:

{
  "name": "get_crypto_price",
  "arguments": {
    "coin_id": "bitcoin"
  }
}

2. analyze_crypto

Get AI-powered analysis of cryptocurrency data with different analysis types.

Parameters:

• coin_id (string): The coin ID to analyze
• analysis_type (string, optional): Type of analysis ("price", "trend", or "comprehensive")

Example:

{
  "name": "analyze_crypto",
  "arguments": {
    "coin_id": "ethereum",
    "analysis_type": "comprehensive"
  }
}

3. get_market_data

Get comprehensive market data for multiple cryptocurrencies.

Parameters:

• coin_ids (array): List of coin IDs
• vs_currency (string, optional): Target currency (default: "usd")

Example:

{
  "name": "get_market_data",
  "arguments": {
    "coin_ids": ["bitcoin", "ethereum", "cardano"],
    "vs_currency": "usd"
  }
}

Setup

Prerequisites

• Python 3.12.4 or higher
• Poetry (for dependency management)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd mcp_server
   

2. Install dependencies:

   poetry install
   

3. Set up environment variables: Create a .env file in the project root:

   GOOGLE_STUDIO_AI_API_KEY=your_google_ai_api_key_here
   

4. Install the package:

   poetry install
   

Usage

Running the MCP Server

The server runs using stdio protocol, which is the standard for MCP servers:

poetry run python -m mcp_server.main

Integration with MCP Clients

To use this server with MCP-compatible clients (like Claude Desktop, etc.):

1. Add to your MCP configuration:

   {
     "mcpServers": {
       "crypto-analysis": {
         "command": "python",
         "args": ["-m", "mcp_server.main"],
         "env": {
           "GOOGLE_STUDIO_AI_API_KEY": "${GOOGLE_STUDIO_AI_API_KEY}"
         }
       }
     }
   }
   

2. Restart your MCP client

3. Use the tools in your AI assistant:

   • Ask for crypto prices: "What's the current price of Bitcoin?"
   • Request analysis: "Analyze Ethereum's market position"
   • Compare multiple coins: "Compare Bitcoin, Ethereum, and Cardano"

API Endpoints

The server uses the CoinGecko API for real-time cryptocurrency data:

• Base URL: https://api.coingecko.com/api/v3
• Rate Limits: Free tier with reasonable limits
• Data Sources: Real-time market data from multiple exchanges

Development

Project Structure

mcp_server/
├── src/
│   └── mcp_server/
│       ├── __init__.py
│       └── main.py          # Main MCP server implementation
├── pyproject.toml           # Dependencies and project config
├── mcp-config.json         # MCP client configuration
└── README.md              # This file

Adding New Tools

To add a new tool to the MCP server:

1. Define the tool in setup_handlers():

   Tool(
       name="your_tool_name",
       description="Tool description",
       inputSchema={
           "type": "object",
           "properties": {
               "param1": {"type": "string"}
           },
           "required": ["param1"]
       }
   )
   

2. Add the handler in handle_call_tool():

   elif name == "your_tool_name":
       return await self.your_tool_method(arguments)
   

3. Implement the tool method:

   async def your_tool_method(self, arguments: Dict[str, Any]) -> CallToolResult:
       # Your implementation here
       return CallToolResult(
           content=[TextContent(type="text", text="Your result")]
       )
   

Error Handling

The server includes comprehensive error handling:

• API Errors: Graceful handling of CoinGecko API failures
• Invalid Input: Validation of tool parameters
• Network Issues: Timeout and connection error handling
• Missing Data: Fallback values for unavailable data

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

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

Support

For issues and questions:

• Create an issue in the repository
• Check the MCP documentation: https://modelcontextprotocol.io/
• Review CoinGecko API documentation: https://www.coingecko.com/en/api