DeFi Yields MCP

Overview

The DeFi Yields MCP is a purpose‑built server that empowers AI agents to discover, filter, and analyze decentralized finance (DeFi) yield opportunities in real time. Leveraging the extensive data catalog of DefiLlama, it exposes a single, well‑documented tool——that retrieves structured pool information across multiple chains and projects. This data is then fed into a specialized analysis prompt, , which instructs an LLM to evaluate key performance indicators such as APY, 30‑day mean APY, and predictive stability classes. The result is a seamless pipeline that transforms raw DeFi metrics into actionable insights for developers, portfolio managers, or any AI‑driven workflow that requires up‑to‑date yield information.

Why It Matters

DeFi ecosystems generate a vast amount of on‑chain data, but extracting meaningful signals from that noise is time‑consuming and error‑prone. The DeFi Yields MCP abstracts away the intricacies of API calls, data normalization, and statistical interpretation. By providing a single entry point for both fetching and interpreting yield data, it eliminates the need to write custom scripts or maintain separate analytical modules. For developers building AI assistants that recommend investment strategies, risk profiles, or portfolio rebalancing, this server delivers the exact data and analysis required without any boilerplate code.

Key Features

• Chain‑and Project Filtering: Specify the blockchain (e.g., Ethereum, Solana) or a particular DeFi protocol (e.g., Lido, Aave) to narrow the search space.
• Rich Data Payload: Each pool record includes TVL, current APY, 30‑day mean APY, and a prediction object that flags expected stability or volatility.
• Analysis Prompt: The prompt instructs the LLM to produce concise, metric‑driven summaries that can be directly consumed by downstream applications or displayed in dashboards.
• Zero‑Configuration Runtime: The server can be launched with a single command, making it trivial to integrate into existing Claude Desktop or other MCP‑compatible AI environments.

Use Cases

• Automated Yield Farming Bots: An AI agent can query the latest high‑APY pools, analyze risk, and automatically generate trade instructions for a bot.
• Portfolio Management Dashboards: Embed the server into a dashboard that updates in real time, allowing managers to see which pools are trending up or down.
• Educational Tools: Build interactive tutorials that let users ask questions like “What are the 30‑day mean APYs for Solana pools?” and receive instant, data‑backed answers.
• Research Pipelines: Researchers can pull large datasets of yield pools, run statistical models on them, and compare performance across chains or protocols.

Integration with AI Workflows

An MCP client simply calls with the desired filters. The JSON payload returned by the tool is then fed into the prompt, which tailors the LLM’s output to focus on the most relevant metrics. Because the server handles all data fetching and formatting, developers can concentrate on higher‑level logic—such as deciding when to rebalance or how to present the analysis—to build sophisticated AI assistants that feel like native DeFi experts.