list_findings

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

No annotations are supplied, so the description carries the full burden. It does add genuinely useful provenance — findings are measured from segmentations rather than asserted in metadata, and counts are returned — but says nothing about whether the listing is exhaustive, how counts are scoped (per case vs per scan), or what the response shape looks like.

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 core purpose and followed by the secondary metadata source. The parenthetical pathology examples are informative rather than filler, and nothing is redundant.

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 zero-parameter, read-only listing with no output schema, the description is largely sufficient: it names the two data sources and the presence of case counts. A brief note on return shape (e.g. how findings are keyed) would close the remaining 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 tool takes zero parameters, so the baseline is 4; there is no argument semantics to explain and the description correctly implies no filtering options exist.

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?

States a specific resource and what it returns: pathology present in the dataset, derived by measuring segmentations, with case counts, plus coarse per-scan metadata categories. That distinguishes it from list_structures (structures) and find_findings (search), though it never names those siblings explicitly.

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?

Usage is implied by 'pathology this dataset actually contains' versus metadata categories, but there is no explicit when-to-use guidance, no statement of when to prefer find_findings or dataset_info, and no prerequisites or exclusions.

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