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

The annotations include 'openWorldHint: true', expecting the agent to handle unknown entities. The description adds the scope (park-based query) and excludes list details, but does not disclose any other behavioral aspects like output format, pagination, or data source limitations. Given the annotation, the description provides minimal additional behavioral context.

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 compact and well-structured, with clear sections for metrics, exclusions, and typical queries. It avoids redundancy and is front-loaded with the primary purpose. The pricing information is included separately but is concise.

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?

Given the tool's simplicity (2 params, 1 required) and the presence of an output schema, the description is mostly complete. It clarifies the metric and what it doesn't cover, and provides typical usage examples. It could mention error cases or data source, but for a simple count query, it is adequate.

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 schema description coverage is 100%, and the description reiterates the park_name examples and year parameter. It adds a bit of authenticity by providing example values, but these are already in the schema. The description does not add significant new meaning beyond what the schema gives, so it fits 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 it queries the count of A-level taxpayer companies for a specific park name. It explicitly distinguishes the metric (A-level taxpayer enterprise count) and what is not included (other company classifications, company list details). This aligns with the naming convention of sibling tools like park_a_taxpayer_company_list, showing it is the count counterpart.

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 typical question formats, indicating when to use this tool (e.g., '中关村软件园A级纳税人企业有多少'). It implies this tool is for count queries, but does not explicitly mention alternatives like the list tool (park_a_taxpayer_company_list). However, the '不包含' (does not include) section clarifies it is not for lists, setting boundaries.

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