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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuinely useful behavior context beyond that: results are ordered as AI-read judgments with textbook quotations and page numbers first, then machine-aligned entries, plus the subject/grade filtering capability.

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 output structure and the use case are front-loaded and every sentence carries information. The trailing English sentence is a bilingual restatement rather than new content, which is minor padding but not harmful.

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?

There is no output schema, and the description compensates well by explaining the two-tier result format and ordering. It does not say what happens when a title matches nothing or how many matches are returned, which is a small remaining gap for a lookup tool.

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 description coverage is 100%, so the baseline is 3, and the description does add value by giving example query titles (「分数的基本性质」「七七事变与全民族抗战」) that show what a well-formed query string looks like. Grade and subject are only mentioned as filters, without format guidance, but the schema already covers those.

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 names a specific verb and resource: given a textbook unit/chapter/lesson title, return its corresponding curriculum-standard points. It also spells out the two-part result (AI per-lesson reading of the textbook text, then machine table-of-contents alignment), which lets an agent distinguish this textbook-to-standard lookup from standard-oriented siblings like get_standard or search_standards.

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?

It gives a concrete usage scenario (「下周教这一课，对应哪条课标」) and states that results can be filtered by subject and grade, so the when-to-use case is clear. It does not explicitly name an alternative tool or a when-not-to-use case, which keeps it short of a 5.

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