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

With no annotations, the description carries the full burden and does substantial work: it discloses that the date is caption-capture date rather than publish date and that the first day contains a one-off backfill, which is a genuine data-quality caveat an agent could not infer elsewhere. It also explains the normalization (rate per million sentences) that makes periods comparable. It stops short of covering pagination, result limits, or authorization.

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 core behavior and bucketing are front-loaded in the first sentence, and the caveats follow in a logical order. The final sentence is long and packs three warnings together, but each clause carries real information rather than filler.

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 six-parameter analytical tool with no output schema and no annotations, the description explains what each period returns (count plus rate per million), the normalization rationale, and the date gotchas. Missing are volume/row limits or default time window behavior, which an agent would still need to guess, but the essential calling context is present.

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%, so the baseline is 3. The description earns above baseline by clarifying the semantics behind the parameters that matter most: the date range refers to caption-capture time, and the first day's counts are inflated by a backfill, which directly changes how since/until should be interpreted. It does not add anything about also_match or channel beyond what the schema already states.

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 verb and resource: 'Count how often something is mentioned over time, by day, week or month.' The unit of measurement (rate per million spoken sentences) is also named. It is clearly distinct from transcript-retrieval siblings like get_youtube_transcript or find_in_transcript, but it does not explicitly contrast itself with the closer analytical siblings such as rank_videos_by_topic.

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?

Provides one concrete use case ('see whether a topic is rising or fading') and strong guidance about date interpretation and the backfill anomaly, which steers correct usage. However, it never states when NOT to use it or which sibling to prefer for related queries (e.g., raw search via search_spoken_words), so the selection decision is only implied.

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