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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds genuinely useful behavior beyond that: 'remarks per answer (no model)' tells the agent the feedback is deterministic rather than LLM-generated, and 'invented answers make the result meaningless' warns against fabricating content on the caller's behalf. It does not say whether the SKILL.md is written to disk or only returned, which matters for an artifact-producing 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?

It is short and front-loads the purpose, so there is no padding. But the density crosses into under-specification: fragments like 'the questions (why + example), written for the person' and 'a starter .ia-eval.yaml' require the agent to reconstruct the sentence structure, which costs more than the words saved.

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?

An output schema exists, and the description still usefully enumerates what each mode returns (questions vs. remarks + SKILL.md + scenarios + starter .ia-eval.yaml), so return-shape needs are covered. For a tool with two modes and a 12-key nested object, though, key facts are missing: whether artifacts are persisted, and what the name parameter determines. Adequate but with clear gaps.

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 50%, and the nested answers object's 12 keys are only described collectively as 'keyed by question id.' The description partially compensates by establishing the question-to-answer flow and noting that answers produce 'remarks per answer,' which explains why answers are keyed by question id. Nothing is said about the top-level name parameter or how partial answer sets are handled, so the coverage gap is only partly closed.

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 opening sentence states a specific verb and artifact: help a person write a SKILL.md they can test. It also names a downstream sibling artifact ('scenarios for test_skill'), which helps separate this from generic generation tools. The compressed fragment style ('No answers: ... / With answers: ...') makes the two operating modes slower to parse than necessary, but the core purpose is unambiguous.

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 a call sequence: call with no answers to get the interview questions, then call again supplying answers to get the SKILL.md and eval scaffolding. It also signals what output feeds test_skill. However, it never explicitly says when to prefer this over directly hand-writing a SKILL.md or over generate_eval_yaml, and it gives no prerequisites or exclusions.

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