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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the read-only nature implicitly via 'Query' and lists what it returns, but it does not mention potential limitations, rate limits, or result structure. This is adequate but not rich.

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 sentences, front-loaded with the tool's core function, and no wasted words. Every piece of detail (return types) is valuable for agent invocation.

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 query tool with a moderate scope, the description covers the key outputs and the input pair. It lacks explicit return format or error behavior, but the listed evidence categories give the agent sufficient context to invoke and interpret results.

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 already provides 100% parameter coverage with clear descriptions (HGNC gene symbol, disease name or EFO term). The description adds no additional parameter-level detail beyond restating the pair context, so a baseline score of 3 is appropriate.

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 specific verb 'Query', the resource 'Open Targets', and the focused scope of 'target-disease association evidence'. It enumerates the types of evidence returned, which differentiates it from sibling tools like query_chembl or query_clinicaltrials.

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 for when to use the tool: querying target-disease association evidence for a gene-disease pair. It does not explicitly name alternatives or exclusion criteria, but the specificity of the use case makes it reasonably clear when this tool is appropriate.

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