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

Annotations already cover read-only and open-world behavior, yet the description adds two things they don't: a credit cost ('Costs 1 credit') and the ranking semantics (ordered by real user search volume). It still doesn't say how many suggestions come back or how cache_max_age affects freshness, so not a 5.

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, definition first, then usage signal, then cost. No filler or repetition of the title.

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 simple suggestion lookup with no output schema, the definition covers identity, use case, and cost. The remaining gap is return shape (how many suggestions, whether they include ranking metadata) and what cache_max_age does, which an agent would still want.

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 75%, with query/region/language self-documented; cache_max_age has no description anywhere. The description's 'partial query' phrasing mirrors the schema rather than extending it, so baseline 3 is right.

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 ('Search typeahead... YouTube's own search suggestions for a partial query') and clarifies it is the autocomplete list, not full search. That distinction separates it cleanly from the sibling youtube_search.

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 a concrete use case ('keyword-research signal') that tells the agent when this is the right call. It stops short of explicitly contrasting with youtube_search or stating when not to use it, so it falls just below the top band.

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