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

Beyond the readOnlyHint annotation, it discloses substantive data semantics: 'all' is ordered first, low-sample entries are suppressed but absence does not mean nobody exists, and logged-in counts may include cloud users while anonymous views count only real people. These are non-obvious counting and filtering caveats the agent could not derive from annotations or schema.

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?

Very compact and front-loaded, with each clause (ordering, suppression, count caveat, workflow, fallback) doing distinct work. The telegraphic arrow/semicolon style and abbreviations like '别拼REST' trade some readability for density but do not pad.

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 read tool with no output schema, it covers what comes back (chips/labels/counts), their ordering and suppression rules, and the natural next steps. Minor gaps remain on the exact response shape and whether counts are scoped by any implicit context, but nothing essential for correct invocation 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 takes no parameters, so the baseline is 4; there is no parameter meaning left to explain. The description appropriately spends its space on return semantics instead.

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 uses a concrete verb ('读') and names the exact resource and returned fields ('人才目录真实chip/label/count'), so the agent knows it is enumerating talent-directory facets rather than people. It is differentiated from siblings via the chain chip→list_talent→get_creator, though 'chip' remains jargon partly carried by the title.

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 an explicit workflow (use a chip here, then list_talent, then get_creator) and an explicit alternative for the edge case ('冷门改search_people'), plus a prohibition ('别拼REST'). When-to-use and what-not-to-do are both stated, leaving little to inference.

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