weakness

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

No annotations are provided, so the description carries the full disclosure burden, yet it never states that this is a read-only/derived calculation, whether it queries across all modules or requires prior data, or what the response looks like. It does helpfully define the non-obvious north-star metric ('同类错误复发率'), which is real added context, but the operational profile is unstated.

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 front-loaded sentences: the profile contents first, the metric definition second. The parenthetical definition is somewhat long but earns its place because the recurrence-rate metric is not self-evident.

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 and no annotations, the description adequately conveys the analytical content returned but leaves the module-parameter behavior and the response shape (e.g., per-cause breakdown format) undocumented. It is viable but incomplete for an agent needing to call it confidently.

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 single `module` parameter (enum reading|listening, optional) is never mentioned in the description, and schema coverage is 0%, so an agent cannot tell what happens if `module` is omitted (aggregate across both? default?). For a one-param tool the schema is trivial, but the description squanders the chance to explain scope selection.

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 identifies this as a weakness-profile analytic output, naming the specific contents: error-cause counts, most-frequent wrong question type, and a recurrence-rate metric. It is distinguishable from operational siblings like ingest_passage or set_cause, though it does not explicitly separate itself from the similarly analytical `report` and `today`.

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?

There is no guidance on when to call this versus `report` or `today`, nor any mention of prerequisites such as whether data must exist first. The agent must infer usage purely from the noun '弱点画像'.

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