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

The description discloses key behaviors: returns total_count plus a capped list (max 200) and a note about popmax breed. With no annotations, this provides essential transparency, though it could mention read-only nature.

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 concise at three sentences, front-loads the purpose, then lists predicates and return info, and ends with a caveat. No redundant content.

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?

The description covers purpose, filter parameters, return format, and a special note. With output schema present, return values are covered. Minor gap: no explanation of the 'limit' parameter.

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?

Given 0% schema coverage, the description compensates by explaining most parameters (esm_max, phylop_min, popmax_min, gene_in, consequence, impact) with meanings. However, the 'limit' parameter is not described.

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 states 'Filtered discovery over all 9.67M variants' and lists predicates, making the purpose specific and distinguishing it from sibling tools like variant_lookup.

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 does not explicitly state when to use this tool versus alternatives like variant_lookup, breed_variant_frequency, or ask. It implies usage for filtered discovery but lacks exclusion criteria or comparative guidance.

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