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

Beyond the openWorldHint annotation, the description adds critical scoping behavior by stating what's excluded: '不包含：其他企业分类的统计；企业名单明细' (does not include: other classification statistics; detailed enterprise list). This clarifies the tool returns a single count only, not a list, and is narrow in scope—useful context that the annotation doesn't provide.

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 main description is well-organized with sections for indicators, exclusions, and example queries, using leading colons for clarity. However, the trailing 'Pricing' block appears to be metadata that leaked into the description, adding noise without semantic value for an AI agent, and could be confusing given credits billing isn't relevant to its task.

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 count-type tool with an output schema, the description is appropriately complete. It specifies what's counted, what's excluded, and provides multiple query examples that cover the main use case. The complexity is low (2 params, simple count output), so additional details like pagination or error cases aren't necessary here.

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 100%, so both year and park_name are documented in the schema. The description enriches park_name with concrete examples like '中关村软件园' and the schema already provides the year format. However, the description largely repeats what's in the schema rather than adding entirely new parameter semantics, keeping this at the baseline of 3.

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 function with a specific verb+resource: '查询当年注销的企业数量' (query the number of deregistered enterprises). It differentiates itself from siblings through the '不包含' (does not include) clause, which clarifies it's a count tool (vs. list tools like park_close_company_list) and specifies the narrow metric scope.

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 at least three concrete example queries ('典型问法') that show exactly how users might phrase questions, including '中关村软件园当年注销的企业有多少'. While it gives implied context and exclusions, it doesn't explicitly name alternative tools to use instead, though the examples and exclusions strongly suggest when to use this tool over siblings.

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