DeFi Yields MCP

by kukapay

15 stars
199 downloads
Not rated
GitHub

About

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

Details

Author
kukapay
GitHub stars
15
Downloads
199
Categories
Other

- Retrieves DeFi yield pool data from DefiLlama.
- Filters yield pools by chain (e.g., Ethereum, Solana) or project (e.g., Lido, Aave).
- Provides an analysis prompt that guides AI agents to examine APY, 30-day mean APY, and predictions.
- Runs directly with uvx – no manual setup beyond configuration.
- Returns structured JSON output for downstream use.

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name DeFi Yields MCP
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

Install the server via uvx mcp install -m defi_yields_mcp --name "DeFi Yields Server" or manually add a configuration to Claude Desktop’s settings. Once configured, use natural language commands such as “Fetch yield pools for the Lido project” or “Analyze yield pools on Ethereum.” The server exposes the get_yield_pools tool and the analyze_yields prompt.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "defi yields mcp": {
            "defi-yields-mcp": {
                "command": "uvx",
                "args": [
                    "mcp",
                    "install",
                    "-m",
                    "defi_yields_mcp",
                    "--name",
                    "DeFi Yields Server"
                ]
            }
        }
    }
}

McpServers

{
    "defi-yields-mcp": {
        "command": "uvx",
        "args": [
            "mcp",
            "install",
            "-m",
            "defi_yields_mcp",
            "--name",
            "DeFi Yields Server"
        ]
    }
}

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.

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