skills-master-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide comprehensive behavioral hints (read-only, non-destructive, idempotent, open-world), so the bar is lower. The description adds valuable context beyond annotations by explaining the AI-powered semantic nature and intent understanding, which helps the agent anticipate how queries are processed. No contradictions with annotations exist.

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 core purpose, followed by usage guidelines, parameters, and examples. Every sentence earns its place without redundancy, making it efficient and easy for an agent to parse quickly.

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 the tool's moderate complexity, rich annotations covering safety and behavior, and 100% schema coverage, the description is mostly complete. It lacks details on output format or result structure, but since there's no output schema, this is a minor gap. The description adequately complements the structured data for agent use.

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?

Schema description coverage is 100%, providing detailed parameter documentation. The description adds minimal value beyond the schema, only mentioning the 'query' parameter with examples but not elaborating on 'response_format'. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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 tool's purpose with specific verbs ('semantic search for skills') and resources ('skills'), distinguishing it from sibling tools like 'search' by emphasizing AI-powered natural language understanding. It explicitly mentions the AI component and natural language queries, which differentiates it from keyword-based alternatives.

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 provides explicit guidance on when to use this tool ('Use this when keywords aren't enough - describe what you want to accomplish'), offering a clear alternative scenario (keyword-based search) and distinguishing it from the sibling 'search' tool. It effectively tells the agent when this tool is preferred over other search methods.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.