cohort_postmarket_stats

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

No annotations are provided, so the description carries full burden. It discloses that rates are per-device with denominator inline and based on a snapshotted cohort. However, it does not mention authentication, rate limits, data freshness, or side effects. The disclosure is helpful but incomplete.

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 a single sentence that front-loads the main purpose. It is efficient but somewhat dense due to the colon and dash. Every phrase adds value, though readability could be improved slightly.

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 one optional parameter and no output schema, the description explains the output metrics and denominator policy. However, it does not cover data source, update frequency, or interpretation guidance. For a statistical tool, additional context would enhance completeness.

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 coverage is 100% for the single optional parameter 'panel', with a description already stating 'Advisory panel, e.g. Radiology; omit for all'. The description adds 'optionally by panel' which aligns with schema, but provides no extra semantic detail beyond what the schema offers.

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 computes postmarket presence rates for an AI/ML device cohort, listing specific metrics (recall, MAUDE trend, drift signal, warning-letter match). It specifies the resource and action, though it doesn't explicitly differentiate from sibling tools like device_postmarket_lookup or evidence_cohort_stats.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies cohort-level analysis, but does not mention exclusions or provide comparison with sibling tools such as device_postmarket_lookup for individual devices.

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