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

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds value by specifying what stats are included ('counts, top skills/locations'), giving the agent a concrete expectation of the return data. No contradictions.

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?

A single, front-loaded sentence with no filler. It communicates the action, resource, and content types in under 15 words.

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 tool with no output schema, the description provides a sufficient high-level summary of what the tool returns. It could specify which counts (e.g., total candidates) or return format, but the stated scope is adequate for an agent to decide usage.

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 input schema is empty (0 parameters), so the baseline is 4. The description doesn't need to explain parameters, and it doesn't invent any. It implicitly confirms the tool takes no arguments.

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 the specific verb 'Get' and identifies the resource as 'the talent corpus' with concrete output details ('counts, top skills/locations'). This clearly distinguishes it from sibling tools like get_candidate or get_contact, which target individual entities.

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 clearly implies the tool is for corpus-level aggregate stats, not individual lookups. While it doesn't explicitly name alternatives or exclusions, the context signals (sibling tool names) make the use case obvious. A slight deduction for not stating 'when not to use' explicitly.

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