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

No annotations are provided, so the description carries the burden of behavioral transparency. It explains that the output is a historical cohort reference, explicitly states that it does not predict whether currently active companies will close, and enumerates the exact metrics returned. It stops short of disclosing potential limitations such as data coverage, filtering semantics, or representative sample selection criteria.

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?

Two dense sentences: the first sets the verb, resource, and output metrics, the second gives use cases and the interpretational caveat. Every phrase earns its place, with the primary subject front-loaded and no filler.

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?

Although the description covers use cases and outputs well, the tool has 21 parameters, 0% schema description coverage, and no annotation-provided context. The output schema partially compensates for the 'return value' side, but the overall guidance is not complete enough for an agent to know how to set filters like tags, scopes, closure types, or funding gap intervals.

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 description coverage is 0% and there are 21 parameters with no descriptions and no enums. The description only vaguely states '按死亡/关闭公司筛选条件' and does not explain the semantics of key parameters such as close_date_start/end, closure_type_ids, last_round_ids, last_funding_gap_days_min/max, or representative_limit. Since the schema provides no help, the description fails to compensate for the extremely low coverage.

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 states a specific action ('聚合分析') on a clearly defined resource ('死亡/关闭公司' with complete financing history) and lists the outputs: sample count, financing event count, total disclosed financing, last-financing-to-closure interval, last-round distribution, closure reason distribution, and representative cases. This clearly distinguishes it from siblings like count_closed_companies or search_closed_companies, which are unidimensional count/list operations.

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 explicit use contexts: failure retrospectives, funding-ineffectiveness analysis, and negative-case due diligence, and adds the exclusion '不预测存续公司是否关闭'. It does not name specific alternative sibling tools, so it stops short of the highest 5 rating, but the context is clear enough for an agent.

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