Folklore Variant Evidence

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds important behavioral context by mentioning explicit limitations, ambiguous-result caution, and the inclusion of provenance—none of which are in the annotations. This goes beyond annotation duplication, though it stops short of describing edge cases or failure modes in detail.

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 focused sentences with no filler. The first sentence states the core function and scope, the second enumerates accepted inputs, and the third provides critical usage warnings. Every sentence earns its place, and the most important information is front-loaded.

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 tool is complex (multiple query syntaxes, output includes ACMG support and evidence), but the description covers input types, output categories, and limitations. The presence of an output schema means return structure doesn't need explanation, and the description fills the remaining context well for a read-only search tool.

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 has 0% description coverage, so the description carries the burden of explaining the query parameter. It does this well by naming accepted formats (coordinates, HGVS, SPDI, rsID) and constraining the variant size/type, which is meaningful information not present in the schema. It could be improved by adding a concrete example or clarifying the exact syntax, but it already adds substantial semantic value.

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 names a specific verb ('Resolve and search') and a precise resource ('one public GRCh38 germline nuclear SNV or simple indel smaller than 50 bp'). It also differentiates the tool by listing accepted query types and output categories, which makes the purpose unmistakable even without siblings to distinguish from.

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 provides clear context on what inputs are appropriate and explicitly warns against patient diagnosis and over-inference from ambiguous results. It lacks an explicit 'when to use vs alternatives' statement, but no siblings exist and the input constraints effectively guide usage.

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