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

Annotations only declare read-only/idempotent/non-destructive; the description adds real behavior: a 6000-character limit, contact details stripped before processing, nothing stored, figures unconfirmed when resume_text is absent, and what the result contains (rubric plus gaps). These are operational facts an agent could not infer from the schema.

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?

Front-loads purpose, then usage, then inputs, then limits and exclusions — a sensible order. The long enumeration of checked items is dense but each item is a distinct rule the agent benefits from knowing; only slight trimming would improve it.

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?

With no output schema, the description does describe the return ('the rubric for the question type and what is missing') and covers limits, privacy handling, and non-persistence. The return shape is described only in one clause, so an agent knows roughly what comes back but not its structure — the main remaining gap.

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 100%, so the baseline is 3; the description still adds value by pairing question_id with its source tool and giving sample question_type values (conflict, failure, stakeholder, prioritization, measure) plus the conditional effect of resume_text. It mainly restates the schema's 'one of question_id and question_type is needed' rule, so it is not fully additive.

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?

States a specific verb and resource ('checks the structure of one practice answer') and enumerates exactly what is checked (situation, first-person action, result, figures, filler, we/I balance). This clearly separates it from siblings like answer_mock_question (which would evaluate content) and check_resume_line (which targets resumes).

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?

Gives a clear trigger ('use it when the person writes or pastes an answer and wants feedback before the interview') plus explicit exclusions ('does not judge whether the story is true', 'does not write the answer'). It stops short of naming a sibling tool as the alternative path, which keeps it below a 5.

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