Target Disease Associations

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

Annotations already declare readOnly, idempotent, and openWorld hints. The description adds that it uses Open Targets and returns a 'full disease landscape,' but does not clarify that the 'limit' parameter may restrict results (creating a minor inconsistency). Overall, behavioral traits are well communicated.

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 three sentences: purpose, use case, and example. It is concise, front-loaded, and every sentence adds value.

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 output schema and annotations, the description covers key aspects: input, output, and example. However, it does not address the 'limit' parameter's effect on completeness (calling it 'full' but limit applies), which is a minor gap.

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 provides detailed descriptions for each parameter (e.g., limit, ensembl_id, uniprot_id), so the description does not need to repeat. The description adds a usage example but no extra semantic value. Baseline of 3 is appropriate due to 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 the tool's function: 'Return all diseases associated with a protein target via Open Targets.' It specifies the input (UniProt accession) and output ('full disease landscape'), and distinguishes it from siblings like get_disease_targets by focusing on target-to-disease mapping.

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 indicates usage context: 'essential for target-validation and indication-expansion.' While it does not explicitly compare to alternatives or state when not to use, the purpose is clear enough to guide appropriate selection.

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