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

The description adds some behavioral context beyond the annotations: it states the tool returns a list of companies (not just a count), and it excludes other company categories. However, the annotation 'openWorldHint: true' is not contradicted. The description doesn't disclose details like pagination, data source, or update frequency, but given the annotation is minimal, the description provides moderate transparency. The pricing information is included but is not behavioral.

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 concise and structured: it starts with the purpose, then lists inclusions/exclusions, and provides typical query examples. The pricing information is appended but is not part of the core description. The description is front-loaded with the main purpose, and every sentence adds value. It could be slightly more structured, but it's efficient.

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 has an output schema (though not shown in detail), the description doesn't need to explain return values. The description covers the purpose, scope, and typical usage. It doesn't mention any prerequisites or limitations beyond the exclusions. For a relatively simple list-query tool, this is fairly complete. The pricing is included, which is extra context.

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%, meaning both parameters (year and park_name) are described in the schema. The description adds examples of park names and clarifies that year is optional. However, it doesn't add much beyond the schema, so the baseline of 3 is appropriate. The description does not explain the format of the year or any constraints beyond what the schema provides.

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 purpose: querying a list of tech-oriented small and medium-sized enterprises based on a specific park name. It specifies the metric/type (科技型中小型企业列表) and provides typical query examples. However, it doesn't explicitly distinguish itself from the sibling tool 'park_tech_oriented_company_num' which likely returns only the count, though the description does mention '仅返回数量不返回名单' as an exclusion, which helps differentiate.

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 clear usage context: it is for querying lists of tech-oriented SMEs in a specific park. It explicitly states what is not included (other company categories, and only returns count not list - though this seems contradictory as the tool is a list tool, but it clarifies the scope). Typical question examples are given, which helps the agent understand when to use this tool. However, it doesn't explicitly mention alternatives or when not to use it, but the sibling tools with similar names (e.g., park_tech_oriented_company_num) imply the distinction.

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