aave-mcp

aave-mcp

3.3

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An MCP server that tracks real-time lending markets on Aave, enabling smart DeFi agents to build automated yield and risk strategies.

AAVE MCP

An MCP server that tracks real-time lending markets on Aave, enabling smart DeFi agents to build automated yield and risk strategies.

Features

  • Tools:
    • Query reserve data (e.g., total liquidity, variable borrow rate).
    • Retrieve user account details (e.g., collateral, debt).
    • Analyze liquidity for specific assets (e.g., USDC, DAI).
    • Placeholder for health factor (extendable with contract calls).
  • Prompt System: Guides LLMs to assess user account risk based on health factor and account data.

Installation

Prerequisites

  • Python 3.10 or higher
  • uv for dependency management
  • A The Graph API key (sign up at The Graph)

Steps

  1. Clone the Repository:

    git clone https://github.com/kukapay/aave-mcp.git
    cd aave-mcp
    
  2. Install dependencies:

    uv sync
    
  3. Installing to Claude Desktop:

    Install the server as a Claude Desktop application:

    uv run mcp install main.py --name "aave-mcp"
    

    Configuration file as a reference:

    {
       "mcpServers": {
           "aave-mcp": {
               "command": "uv",
               "args": [ "--directory", "/path/to/aave-mcp", "run", "main.py" ],
               "env": { "THEGRAPH_API_KEY": "the_graph_api_key"}               
           }
       }
    }
    

    Replace /path/to/aave-mcp with your actual installation path, and the_graph_api_key with your API key from The Graph.

Tools

The server provides the following tools:

Tool NameDescriptionParameters
get_reservesQueries AAVE V3 reserve data (e.g., liquidity, borrow rates).
get_user_dataRetrieves user account data (e.g., collateral, debt).user_address: str - Ethereum address.
get_user_health_factorPlaceholder for user health factor (extend with contract call).user_address: str - Ethereum address.
analyze_liquidityAnalyzes liquidity for a reserve asset (e.g., USDC).symbol: str - Asset symbol.

Additionally, the analyze_user_risk prompt guides LLMs to assess user risk based on health factor and account data.

License

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