skills_for_task

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

With no annotations, the description carries the full disclosure burden and provides almost none: it does not say what is returned (names only, paths, descriptions), whether results are ordered, or whether the tool is a pure read. For a low-risk lookup this is not dangerous, but an agent cannot predict the response shape.

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?

Two short sentences with the purpose front-loaded and the value list compact. The telegraphic phrasing ('task_type:') is efficient, though it reads slightly under-specified rather than deliberately terse.

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?

For a single-parameter recommendation lookup with no output schema, it covers input values well and gives a rough sense of the return ('skills to load'). It stops short on return shape and on whether task_type is a closed set, which an agent deciding between this and list_skills would want.

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 coverage is 0% and the schema declares task_type as a bare string with no enum, so the description's enumeration (web, api, mobile, cloud, llm, agent, data, pipeline, org) is the only source of valid values — a substantial add over the schema. It does not clarify whether other values are accepted or what happens on an unrecognized task_type.

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 phrase 'Which skills to load before building something' conveys a recommendation lookup keyed by task type, which separates it from list_skills and get_skill in spirit. However, it names no verb (get/recommend/list) and never states that it returns a curated skill set for the given task_type, so the agent must infer the output.

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?

'before building something' gives a timing cue that implies when to call it, which is genuine usage guidance. It never mentions when NOT to use it or why an agent would pick this over list_skills/get_skill, leaving the sibling routing entirely implicit.

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