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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), yet the description still adds real substance: per-year video-count gating, that sparse years return null rather than being dropped or erroring, and that "year" means catalogue published_at. That is exactly the kind of behavioral detail annotations cannot carry.

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?

Three short sentences with no filler, and the most decision-relevant fact (sparse years are null) is stated early. The opening sentence is elliptical about its object, so brevity costs a little clarity.

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?

An output schema exists, so return values need not be explained, but this is a complex aggregating tool with six parameters, nested filters and heavy enum use, all undocumented. An agent cannot choose a metric or predict how filters interact with the per-year minimum from this description alone.

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 6 parameters including a 9-property nested filter object and a 13-value metric enum. The description hints at the min_videos threshold and at the date basis behind year_from/year_to, but never names them, and says nothing about metric, role, limit or cursor. It does not compensate for the coverage gap.

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?

"Year-by-year descriptive distributions" conveys the aggregation axis (year) but not the subject – which metric, from which corpus. The metric is only discoverable in the schema. It also does not distinguish itself from near-siblings like corpus_stats, typical_structure or compare_groups.

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 statement of when to call this instead of corpus_stats or compare_groups, and no prerequisites or exclusions. The agent must infer usage purely from the name and the enum list.

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