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

No annotations are available, so the description carries the full behavioral burden. It discloses the response contract: two numbers representing matching and indeterminate records, and instructs the agent to report both. It also gives corpus size and sign-range context, though it omits details like auth, rate limits, or the precise definition of indeterminate.

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?

Three short sentences with no filler: purpose, output contract, and sibling routing are all present and front-loaded. Every clause earns its place.

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 output schema exists and the input schema defines enums and limits, the description fills the main remaining gaps: what is counted and how to present the result. The undefined 'indeterminate' category and unmentioned lang parameter are minor gaps that are largely recoverable from the schema and enums.

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%, so the description must compensate. It meaningfully explains znak (0–11, 0 = Aries) and ties 'graha' to the graha parameter, but it does not explain the lang parameter or the graha enum values beyond their existence. The compensation is partial rather than complete.

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 concrete task: identify which famous people in a 76k-person corpus were born with a given graha in a given sign and count them. It also specifies the output contract and distinguishes the tool from research_query by pointing comparison use to that sibling. The purpose is unmistakable despite the informal phrasing.

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 explicitly routes the comparison use case to research_query, effectively telling an agent when corpus_search is not the right tool. It does not enumerate all alternatives or exclusions, but the stated boundary covers the most relevant sibling ambiguity.

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