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

The annotation only provides openWorldHint: true, offering minimal behavioral context. The description compensates well by specifying the tool's behavior: it returns change indicators and notes that versions are soft constraints subject to real library versions with version_mismatch flagging. It also mentions pricing in the credits metadata, adding value. However, it doesn't fully describe return structure or edge cases (e.g., no data found), but given the output schema exists, this is acceptable.

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 well-structured: it starts with the core purpose, enumerates coverage, then exclusions, then example queries. The pricing is included in a separate metadata block, which is appropriate. It could be trimmed slightly (the pricing detail is duplicated as metadata), but overall it's efficient and front-loaded.

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?

The tool is moderately complex (3 params, output schema, multiple query modes). The description covers key aspects: what it queries, what's excluded, typical questions, version behavior, and pricing. It doesn't over-explain since the output schema exists. It could benefit from mentioning whether results are aggregated or per-region, but overall it's sufficiently complete for an agent to route 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 100%, so the schema documents all parameters. The description adds context by explaining the indicator intent and provides the functional role of gov_names across query types (point/compare, rank/list/filter, peer_rank), and how input_text is used when gov_names is absent. It also gives a concrete example. This goes beyond schema definitions, which typically just name parameters.

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 states the tool queries region-level enterprise change indicators (new registrations, cancellations, revocations, growth). It explicitly covers what's included (new counts/ratios, cancellations/revocations, 2-year growth, survival years structure) and what's excluded (stock-scale queries, redirecting to 市场主体规模). The verb + resource + scope is specific and distinguishes it from siblings.

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 provides explicit guidance on when to use the tool: '典型问法' (typical queries) with examples, and explicitly states the exclusion ('不含存量规模点查主口径（请用市场主体规模）') directing to the appropriate sibling tool (gov_data_enterprise_scale). This is clear and actionable.

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