DeFi Yields MCP | MCP Market

DeFi Yields MCP

Сервер MCP, позволяющий AI-агентам исследовать возможности DeFi доходности с помощью DefiLlama. Включает инструменты для поиска и анализа пулов доходности по различным метрикам.

DeFi Yields MCP

An MCP server for AI agents to explore DeFi yield opportunities, powered by DefiLlama.

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Features

  • Data Fetching Tool: The get_yield_pools tool retrieves DeFi yield pool data from the DefiLlama, allowing filtering by chain (e.g., Ethereum, Solana) or project (e.g., Lido, Aave).
  • Analysis Prompt: The analyze_yields prompt generates tailored instructions for AI agents to analyze yield pool data, focusing on key metrics like APY, 30-day mean APY, and predictions.
  • Packaged for Ease: Run the server directly with uvx defi-yields-mcp.

Installation

To use the server with Claude Desktop, you can either install it automatically or manually configure the Claude Desktop configuration file.

Option 1: Automatic Installation

Install the server for Claude Desktop:

uvx mcp install -m defi_yields_mcp --name "DeFi Yields Server"

Option 2: Manual Configuration

Locate the configuration file:

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

Add the server configuration:

{
 "mcpServers": {
   "defi-yields-mcp": {
     "command": "uvx",
     "args": [ "defi-yields-mcp" ]
   }
 }
}

Restart Claude Desktop.

Examples

You can use commands like:

  • "Fetch yield pools for the Lido project."
  • "Analyze yield pools on Ethereum."
  • "What are the 30-day mean APYs for Solana pools?"

The get_yield_pools tool fetches and filters the data, while the analyze_yields prompt guides the LLM to provide a detailed analysis.

Example Output

Running the get_yield_pools tool with a filter for Ethereum:

[
  {
    "chain": "Ethereum",
    "pool": "STETH",
    "project": "lido",
    "tvlUsd": 14804019222,
    "apy": 2.722,
    "apyMean30d": 3.00669,
    "predictions": {
        "predictedClass": "Stable/Up",
        "predictedProbability": 75,
        "binnedConfidence": 3      
    }
  },
  ...
]

License

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