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

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the return payload (totals, open count, distribution, data source count, recent additions, as-of date) and implies a read-only overview. However, it does not explicitly state read-only behavior, data freshness policy, or definitions of terms like '库容'.

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: the first sentence defines scope and gives a usage example, the second enumerates the return fields. Every sentence adds value and the key purpose is front-loaded.

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 no parameters, an output schema is present, and the description covers purpose, usage context, and return fields, an agent has everything needed to select and invoke it correctly. No critical information is missing.

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 tool has zero parameters and the schema is empty, so there is nothing to document. The baseline for 0-parameter tools is 4, and the description appropriately adds no parameter information because none is needed.

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 identifies the tool as a data overview ('数据总览') and enumerates the metrics it covers: capacity, open positions, enterprise-type distribution, data source count, and recent additions. It also states what is returned. It does not explicitly contrast itself with sibling tools, but the aggregate-stats nature is distinguishable from search/detail/deadline 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 a concrete use case: answering questions like '国央企校招现在整体什么情况' (what is the overall situation of state-owned enterprise campus recruitment). This is clear context for when to use the tool, though it does not name alternatives or state when not to use it.

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