Genomic-Agent-Discovery

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

No annotations provided, so description carries full burden. It discloses what data fields are returned (traits, p-values, effect sizes), providing output transparency. However, lacks details on error handling (e.g., invalid rsID), authentication requirements, or rate limiting.

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?

Two efficient sentences with zero redundancy. Front-loaded with the core action ('Look up GWAS Catalog associations'), followed by specific return value documentation. Every word 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?

For a single-parameter lookup tool without output schema, the description adequately compensates by listing specific return fields (traits, p-values, effect sizes). Would benefit from mentioning error cases (e.g., rsID not found) or data coverage limitations, but sufficient for tool selection.

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 'rsid' fully described as 'dbSNP rsID'. Description mentions 'variant' which loosely maps to the parameter, but adds no additional format constraints, examples, or validation rules beyond the schema. Baseline 3 appropriate for complete 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?

Specific verb 'Look up' + resource 'GWAS Catalog associations' + target 'variant' clearly defines scope. The enumeration of returned data types (traits, p-values, effect sizes, study references) distinguishes it from clinical interpretation siblings like query_clinvar or query_civic.

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?

Specifies the domain (GWAS Catalog, genome-wide association studies) which implies usage for population genetics research, but lacks explicit when-to-use guidance or contrasts with alternative tools like query_snpedia or query_civic.

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