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

The description makes the query nature clear and discloses scope boundaries: only the metric of companies with external financing, only for a specific park, and no list-level detail. The openWorldHint annotation is not contradicted, and the '查询' framing implies a read operation; however, details such as empty-result behavior or exact time-window interpretation are not described.

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 well organized with sections for the metric, exclusions, and typical questions. The three example questions are somewhat repetitive but serve as useful invocation patterns. The appended pricing block is extra operational detail but does not significantly hurt clarity.

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 an output schema present and clear metric/exclusion information, the tool is mostly usable. However, ambiguity around the optional year versus the fixed '近两年' window is not resolved, and there is no explicit pointer to list/chain alternatives, leaving a noticeable completeness gap for an AI agent.

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 the schema already documents both park_name and year. The description adds example park names and the '近两年' temporal context, but it does not clarify how the optional year interacts with the 'past two years' window, which is a meaningful semantic gap.

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 counts companies with external financing in the past two years for a specific park name. It names the exact metric, explicitly excludes list details and other categories, and uses typical questions that disambiguate it from the many sibling *_num and *_list tools.

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 concrete example questions and explicitly states exclusions ('不包含：其他企业分类的统计；企业名单明细'), which helps the agent know when not to use this tool. It does not explicitly name alternative tools like park_invested_company_list or chain_invested_company_num, so it stops short of full when/when-not guidance.

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