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

Annotations already declare readOnly/openWorld=false/idempotent/non-destructive, and the description adds genuinely new traits: the dataset is offline, capped at 40 rows, and every row carries its sources. Those operational constraints are not derivable from the annotations.

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?

Front-loaded with the verb and resource, and each sentence earns its place (topic semantics, sourcing, usage, alternative, row limit). It is dense and long, but no sentence is pure 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?

With no output schema, the description carries the return-value burden and does so by describing what each topic yields, plus the source-per-row guarantee and the 40-row cap. Usage, alternatives and limits are all covered for a zero-required-param lookup tool.

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 baseline is 3. The description goes beyond the enum labels by explaining the shape of each topic's output (programs returns eligibility thresholds; rpm tables return low/high/median per 1,000 views). It adds meaning for the enum but says little extra about the niche/country/platform filters, which the schema already covers.

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 ('look up') and a concrete resource ('sourced 2026 creator-income data'), then decomposes exactly what each topic returns. It explicitly contrasts itself with the sibling estimate_youtube_earnings, so an agent can select it without opening either schema.

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?

Gives explicit use cases ('how much does X pay', 'what do I need to monetize') and names the condition that routes to the alternative ('for a concrete earnings estimate from a view count use estimate_youtube_earnings instead'). Nothing is left to inference.

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