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

The description makes the query-only nature clear (“查询”) and adds useful behavior context: coverage boundaries and a precise per-data-unit pricing formula based on region × indicator × date version. It does not mention rate limits or auth requirements, but for a data-query tool the disclosed behavior is sufficiently complete and does not contradict the openWorldHint annotation.

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 front-loaded with the main purpose, then compactly covers the coverage area, exclusion, typical questions, and pricing. Every sentence adds decision-relevant information; the structured pricing JSON is verbose but necessary for cost awareness. No filler or repetition.

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 moderately scoped macro-indicator query tool, the description is complete enough: it names covered subjects, gives exclusions, presents typical intents, and communicates billing constraints. The schema handles parameter details such as version mismatch semantics, and the output schema can define the return shape.

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 input schema already covers all 3 parameters with meaningful descriptions, including versions, gov_names, and input_text semantics. The tool description's billing meter adds context about region × indicator × date units but does not substantially change or expand the meaning of the parameters beyond the schema, so a baseline 3 is appropriate.

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 uses a clear verb and resource: it queries regional innovation and intellectual-property macro indicators. It further disambiguates by listing the covered domains (high-tech/innovative enterprise counts, patents/R&D, tech services) and explicitly excludes general all-industry enterprise counts, which distinguishes it from siblings like gov_data_enterprise_scale.

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 explicit when-not-to-use guidance: “不含普通全行业企业存量总览（请用市场主体规模）”, and provides typical query phrasings such as “某区高新技术企业数量” and “专利相关指标TOP城市.” This makes selection versus sibling gov_data_* and chain_* tools unambiguous.

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