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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the bar is lower; the description still adds real context the annotations cannot: equal weighting per video, missing measurements remaining unknown rather than imputed, and cited median/extreme examples. The one behavioral lever it omits is allow_broaden, whose effect on the result set is never disclosed.

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 dense sentences with no filler, and the statistical contract (dispersion, median/extremes) is front-loaded. The compression occasionally costs clarity, but 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?

The presence of an output schema excuses it from explaining return values, but a five-parameter tool with a nested ten-field filter object and a zero-coverage schema needs far more. The absence of any explanation of allow_broaden or of how role and metric interact leaves an agent unable to call this correctly without guessing.

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% across five parameters, including a nested filters object with ten fields, so the description must carry the burden and does not. It never mentions role, metric, the filter dimensions, or min_videos, and allow_broaden — the parameter most likely to change results unexpectedly — is left entirely unexplained.

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 conveys that the tool produces aggregate statistical summaries (dispersion, median, extremes) for a chosen metric across the corpus, which is more than the title alone. However, the verb 'describe' is vague and the phrasing is compressed enough that the core action (compute corpus-level statistics for a metric) must be inferred. No sibling is named, despite near-neighbors like compare_groups, trends, and typical_structure.

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?

There is no explicit guidance on when to use this versus compare_groups, compare_to_reference, trends, or typical_structure, all of which operate on aggregate corpus data. The eight-video minimum implies a sample-size prerequisite but is stated as an output rule rather than a usage condition, and no exclusions or alternatives are offered.

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