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

The annotations only include openWorldHint: true, so the description carries the burden of behavioral disclosure. It adds clarity by stating that the tool returns a count and not details, and explicitly excludes other category statistics and list details. This is helpful scope transparency but does not disclose any side effects, limitations, or additional behavior beyond what is already implied by the schema and 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 concise and well-structured: a clear purpose statement, an indicator line, an exclusions line, typical questions, and pricing. It is front-loaded with the core function and avoids any unnecessary information. Every sentence serves a purpose, making it highly efficient and easy to scan.

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 that an output schema exists (so return format is covered elsewhere), the description is fairly complete for this simple counting tool. It covers the query context, typical usage, and exclusions. It could be slightly more explicit about how the year parameter behaves (e.g., default to current year if omitted), but the schema already describes it. Overall, it provides sufficient context for an agent to invoke the tool correctly.

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 provides full descriptions for both parameters (park_name and year), and schema description coverage is 100%. The description adds example park names and year usage in typical questions, but this does not add significant semantic value beyond the schema. Per the rubric, a baseline of 3 is appropriate when schema coverage is high and the description offers marginal reinforcement.

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: to query the number of newly added enterprises in a specific park for the current year. It distinguishes itself from sibling list tools and other metrics by specifying that it returns a count, not details, and that it only covers new start companies. This is specific to the resource (park) and metric (new start count), making the purpose unmistakable.

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 examples (e.g., 'How many new enterprises did Zhongguancun Software Park add this year?') which illustrate when to use the tool. It also states exclusions (does not include other enterprise categories or enterprise list details), guiding users away from inappropriate usage. However, it does not explicitly name alternative tools like park_new_start_company_list, so it falls short of the highest bar of explicit alternative guidance.

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