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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds behavioral context by listing the evidence categories (genetics, expression, animal models, drugs, literature, pathways) and that results are 'scored' and 'top'. No contradiction with 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 moderately concise but front-loaded with example queries that make the purpose immediately clear. The evidence list adds length but provides useful context; no redundant or empty phrases.

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?

With only 2 parameters, a rich output schema, and annotations, the description fully covers the tool's purpose, input format, and use case. It doesn't describe output details, but the output schema handles that, making the description adequate for the tool's complexity.

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 schema only describes `size`, leaving `ensembl_id` undocumented. The description fills this gap by specifying it as an 'Ensembl gene ID' and showing examples. It does not add detail for `size`, but schema covers it, so the description meaningfully compensates for the missing param.

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 opens with explicit natural-language queries ('What diseases is [gene] involved in') and then precisely states the tool returns 'top disease associations for a drug target (Ensembl gene ID), scored by integrated Open Targets evidence'. It clearly distinguishes from sibling tools like disease_associations by specifying the target-centric direction and unique evidence integration.

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 explicitly frames the intended use case: 'Use to triage what diseases a candidate target might address.' This gives a clear context for when to invoke. However, it does not name alternative tools or exclusion conditions, so it stops short of full when/when-not guidance.

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