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

Beyond the annotations, the description discloses important behavioral context: the returned fields, the distinction between filing (备案) and registration (登记), that neither grants approval, that filings are not distinct models or companies, and that no match does not prove absence. These are substantive caveats that materially affect interpretation.

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 description is dense but front-loaded with the search action, then filters, return shape and caveats. Every sentence carries useful information, though the single-paragraph format could be more scannable.

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 six-parameter, read-only lookup with no output schema, the description covers searchable fields, filter behavior, return columns, and crucial interpretation limits. It is complete enough for an agent to invoke and interpret results correctly.

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 already 100%, so the baseline is 3. The description still adds meaning beyond the schema by clarifying which fields q searches, that brand uses an exact filer legal-name allowlist, that us_list uses confirmed tier A crosswalks, and that province applies only to generative AI rows with a CAC province field.

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 states a specific verb and resource: search the CAC public generative AI, deep-synthesis and algorithm lists by named fields. It is clear what the tool does, but it does not explicitly differentiate itself from the related sibling china_ai_us_lists, leaving a small gap against the top score.

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 filter-level usage guidance, such as registry defaulting to all and province applying only to generative AI rows, and explains us_list and brand semantics. However, it never explicitly states when to use this tool instead of alternatives like china_ai_us_lists, so usage is implied rather than fully directed.

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