defi-yields-mcp
Provides a community support channel for users of the DeFi Yields MCP server, accessible via a Discord invite badge in the README.
Fetches and filters DeFi yield pool data from Ethereum blockchain, allowing analysis of APY, TVL, and yield predictions for various DeFi projects.
Retrieves yield pool data from the Solana blockchain, enabling analysis of APY metrics and yield opportunities across Solana-based DeFi projects.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@defi-yields-mcpshow me the top yield pools on Ethereum by APY"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
DeFi Yields MCP
An MCP server for AI agents to explore DeFi yield opportunities, powered by DefiLlama.
Features
Data Fetching Tool: The
get_yield_poolstool 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_yieldsprompt 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.
Related MCP server: Solana DeFi Intelligence MCP Server
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.jsonWindows:
%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.
Available Tools
1 toolget_yield_poolsA
Fetch DeFi yield pools from the yields.llama.fi API, optionally filtering by chain or project.
Returns symbol, project, tvlUsd, apy, apyMean30d, and predictions for each pool.
Args:
chain: Optional filter for blockchain (e.g., 'Ethereum', 'Solana')
project: Optional filter for project name (e.g., 'lido', 'aave-v3')
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | ||
| project | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool fetches data (read operation) and returns specific fields, but lacks details on rate limits, authentication needs, error handling, or pagination. It adds basic context but misses key behavioral traits for an API call tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the main purpose, followed by return details and parameter explanations. It uses three concise sentences with no wasted words, efficiently conveying necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 2 parameters, the description is moderately complete. It covers purpose, parameters, and return fields, but lacks output structure details, error cases, or advanced usage context. It's adequate for basic use but has gaps for full agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explaining both parameters ('chain' and 'project') with examples (e.g., 'Ethereum', 'lido'), clarifying their optional nature and usage. This adds meaningful semantics beyond the bare schema, though it doesn't cover all potential nuances like format constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Fetch DeFi yield pools') and resource ('from the yields.llama.fi API'), with optional filtering capabilities. It distinguishes the tool's function well, though without sibling tools, differentiation isn't applicable. The purpose is specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through the optional filters ('optionally filtering by chain or project'), suggesting when to apply them. However, it lacks explicit guidance on when to use this tool versus alternatives, prerequisites, or exclusions. With no sibling tools, context is limited to implied filtering scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v1.0.0- First observed
get_yield_pools
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'get_yield_pools' has a clearly defined singular purpose of fetching DeFi yield pools with optional filters.
The single tool name 'get_yield_pools' follows a clear verb_noun pattern that would be consistent if there were more tools. There are no naming inconsistencies to evaluate in a one-tool set.
A single tool is insufficient for comprehensive DeFi yield analysis. While the tool provides basic fetching capabilities, the domain suggests needs for additional operations like historical data, comparisons, or pool-specific details that are missing.
The tool surface is severely incomplete for DeFi yield analysis. It only offers data fetching without any CRUD operations, filtering by more parameters like risk or type, or analytical tools for processing the yield data, leaving significant gaps for agent workflows.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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