defi-yields-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
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.
Naming Consistency5/5The 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.
Tool Count2/5A 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.
Completeness2/5The 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.
Average 3.5/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior3/5
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.
Conciseness4/5Is 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.
Completeness3/5Given 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.
Parameters4/5Does 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.
Purpose4/5Does 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.
Usage Guidelines3/5Does 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.
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