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

Beyond the openWorldHint annotation, the description discloses the exact included indicators and exclusions, and provides typical request phrasings to show expected subject-verb-object structure. The phrase '周期变化' is slightly ambiguous regarding whether returns are time-series changes or current statuses, but the enumerated indicators and examples make the intended behavior reasonably clear and the description does not contradict annotations.

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 core action and negative boundaries, then provides a useful indicator list and realistic example phrasings. It is longer than minimal but each section serves a decision-relevant purpose; no substantial redundancy judged. The pricing block is extra metadata but does not seriously harm readability.

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 only two parameters, an output schema present, and a large sibling family, the description is adequately complete for selection: it clearly scopes this tool to enterprise-level fiscal support questions and separates it from industry-policy and government-visit interactions. It does not name close alternatives such as company_acquired_subsidy, which leaves some possible ambiguity in the broad sibling list, hence not a 5.

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 coverage is already 100%: country_name and company_name both have illustrative examples in the input schema. The description's typical questions reinforce the expected pairing (e.g., 日本 + Toyota, 中国 + 比亚迪) but do not substantially add semantic meaning beyond what the schema already provides, so this stays at the high-coverage baseline.

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's job: given a specific enterprise name, query government-enterprise policy/fiscal support status, enumerating the three covered indicator types (R&D funding/subsidies, tax incentives, industry-technology cooperation policy support). It also explicitly excludes industry-level policy queries, government-visit interaction, and batch screening, which distinguishes it from relevant siblings such as enterprise_change_external_policy and enterprise_change_gov_visit_exchange.

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 gives explicit when-to-use guidance (by specific enterprise/country) and several explicit when-not-to-use conditions: not for industry-policy changes, not for government-visit/communication interactions, and not for batch filtering by park or industry chain. It does not name alternative tools explicitly, so it stops 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.