skillNinja_recommend

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets' recommendations, implying a read-only operation, but doesn't clarify aspects like whether it requires authentication, has rate limits, returns structured data, or handles errors. For a tool with no annotations, this is a significant gap in transparency.

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 concise and front-loaded with the primary English statement, followed by a Japanese translation. The English part is a single, clear sentence that states the purpose efficiently. However, the Japanese translation adds redundancy without new information, slightly reducing efficiency, though it doesn't significantly hinder clarity.

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 complexity (a recommendation system with one parameter) and the absence of annotations and output schema, the description is incomplete. It doesn't explain what 'popularity' means, how recommendations are generated, the format of the output, or any behavioral traits. For a tool with no structured support, this leaves critical gaps for an agent to understand and use it effectively.

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?

The input schema has 100% description coverage, with the single parameter 'workspacePath' documented as 'Absolute path to the workspace directory.' The description adds no additional meaning about parameters, such as how the workspace path influences recommendations. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.

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: 'Get skill recommendations based on popularity.' It specifies the verb ('Get') and resource ('skill recommendations') with a qualifier ('based on popularity'). However, it doesn't explicitly differentiate from sibling tools like skillNinja_search or skillNinja_list, which might also retrieve skills, so it doesn't reach the highest clarity level.

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 no guidance on when to use this tool versus alternatives. It doesn't mention siblings like skillNinja_search (which might filter skills) or skillNinja_list (which might list all skills), nor does it specify contexts or exclusions for using this recommendation tool. This leaves the agent with minimal usage direction.

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