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

Annotations already indicate readOnlyHint and closed-world semantics, but the description adds crucial behavioral details: it refuses when no graph path exists and never fabricates evidence. This goes beyond the structured annotations and is highly valuable for an agent.

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 compact and front-loaded: first sentence states the function, gives examples, and describes the output; second sentence states the refusal behavior. No filler or redundant 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?

With read-only and closed-world annotations plus an output schema, the description covers the core behavior, refusal mode, and typical use cases. The only notable gap is the lack of explanation for the optional parameters, but the tool is still quite complete for selection and basic invocation.

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 description coverage is 0%, so the description must compensate. It explains the query parameter via natural-language examples, but says nothing about hpo_ids or max_hops, leaving two of three parameters underspecified.

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 performs graph-grounded reasoning and returns multi-hop evidence paths with citable provenance. Concrete examples ('why does gene X associate with disease Y?') make the scope unambiguous and distinguish it from sibling tools like get_evidence or get_hypotheses.

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 gives clear usage context: use when a natural-language question requires multi-hop explanation over the graph, with representative question formats. It does not explicitly name alternatives or exclusions, so it stops short of a 5.

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