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D
Defi Yields MCP

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

Created 4/19/2025
Updated about 2 months ago
Repository documentation and setup instructions

DeFi Yields MCP

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

Discord GitHub License Python Version Status

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.