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

Annotations already declare readOnly=true, destructive=false, openWorld=false, so the safety profile is covered. The description adds real behavioral value by enumerating the six specific data-volume figures surfaced and framing the tool's role as explaining 数据边界, which annotations cannot convey.

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 sentences with zero waste: the first front-loads purpose and the returned metrics, the second gives usage triggers. Nothing repeats the name or title gratuitously.

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?

For a zero-parameter stats tool with no output schema, the description supplies both the content (six metrics) and the invocation context (data-completeness questions), which is sufficient for an agent to call and explain it 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 tool takes zero parameters and the schema is empty, so there is nothing to document; baseline is 4. No parameter meaning is needed or missing.

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?

States a specific verb (获取) plus resource (数据体量概览) and then enumerates the exact metrics returned: 在招岗位数, 招聘企业数, 覆盖地区数, 求职攻略数, 题库量, 薪资记录数. This clearly distinguishes it from the sibling detail/search tools (get_job_detail, search_campus_jobs).

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?

Gives explicit usage triggers: when the user asks 「有多少校招岗位」, 「数据全不全」, or when the agent needs to explain data boundaries. Clear context for invocation, though it does not name sibling 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.