Genomic-Agent-Discovery

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

No annotations are provided, so the description carries the full burden. It adds valuable behavioral context by specifying the return format ('predicted pathogenicity class and score (0-1)'), but omits other behavioral traits like error handling when rsID is not found, rate limits, or authentication requirements.

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 consists of two efficient sentences with zero waste. The first sentence states purpose immediately; the second discloses return values. Every word earns its place and the structure is optimally 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?

For a single-parameter lookup tool with 100% schema coverage, the description is complete. It compensates for the missing output schema by describing the return values (class and score). Could be improved by mentioning error cases (e.g., rsID not found), but sufficient for the complexity level.

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 coverage is 100% with the 'rsid' parameter fully documented as 'dbSNP rsID'. The description implies input by referencing 'missense variant' but does not explicitly discuss the rsid parameter or add semantic details beyond the schema. Baseline 3 is appropriate given high schema coverage.

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 specific verbs ('Look up') and identifies the exact resource ('AlphaMissense AI pathogenicity prediction') and context ('for a missense variant'). It clearly distinguishes from sibling query tools by specifying the AlphaMissense algorithm specifically.

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 implies usage by specifying the AlphaMissense algorithm, but provides no explicit when-to-use guidance compared to siblings like query_cadd, query_clinvar, or query_civic that also provide pathogenicity predictions. No alternatives or exclusions are mentioned.

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