Cross-Disease Structural Target Overlap

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

Annotations indicate readOnlyHint, openWorldHint, and idempotentHint, and the description's verb 'identifies' and 'returns' confirms non-destructive behavior. The description adds transparency by detailing the return structure (shared, unique, Jaccard score), which goes beyond 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 well-structured with a clear first sentence, bullet points for outputs, and a code example. Every sentence adds value, no filler, and the format is easy to parse.

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?

Given the tool's moderate complexity (two disease inputs, multiple outputs including Jaccard score) and the presence of an output schema, the description covers all necessary aspects: purpose, use cases, outputs, and an example. It is thorough without being redundant.

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 already includes detailed descriptions for all three parameters (mondo_id_a, mondo_id_b, target_limit). The description provides an example with inline comments but adds little new semantic information beyond the schema. Baseline 3 is appropriate given high 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?

The description clearly states it compares protein target landscapes of two diseases, lists specific outputs (shared targets, unique to each, Jaccard similarity), and provides an example with comments. It distinguishes itself from siblings like get_common_disease_targets by its specific analysis and return types.

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 mentions use cases such as drug repurposing, identifying shared mechanisms, and understanding comorbidity, guiding the agent on appropriate use. However, it does not explicitly state when not to use or compare with sibling tools, slightly reducing clarity.

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