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

Beyond the readOnlyHint annotation, the description discloses that results are generated via guilt-by-association, are hypotheses only, and are traceable via gene and disease. This adds meaningful behavioral context without contradicting the annotations.

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 a single, dense sentence that efficiently conveys purpose, method, and caveat. It avoids unnecessary fluff while packing essential information.

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 read-only, single-parameter tool with an existing output schema, the description provides strong contextual completeness: it explains what the tool does, how results are generated, and the crucial caveat that they are hypotheses. The only minor gap is the implicit parameter definition.

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 zero description coverage, but the description implies the 'orphaCode' parameter refers to the disease in question via 'compartilham genes com esta.' This is not explicit, so the parameter semantics are only partially addressed.

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 the tool returns drug repositioning candidates generated by gene association (guilt-by-association), and explicitly distinguishes these as hypotheses, not clinical recommendations. This specificity differentiates it from sibling tools like get_evidence or find_similar_diseases.

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 the tool is for investigating drug repositioning hypotheses and includes a strong warning that results are never clinical recommendations. However, it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria.

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