TikTok industry averages for a business category

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

Annotations already declare readOnlyHint and non-destructive behavior. The description adds valuable context beyond annotations: the data is TikTok's own cross-account averages (not the brand's), it's free, and authorization is required. This enriches the behavioral picture without contradicting any hints.

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 description is concise and well-structured: it front-loads the purpose, then lists metrics, provides pairing guidance, and ends with constraints and auth. Each sentence earns its place, though it could be slightly tightened without losing meaning.

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?

There is no output schema, but the description enumerates the exact metrics the response will contain (mean likes, comments, shares, etc.), and clarifies the data source and authorization requirement. This is sufficient for an agent to understand what to expect, though it doesn't specify format or pagination, which is minor for a benchmark endpoint.

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?

The schema has a complete enum of 25 allowed values for businessCategory, so the parameter is self-documenting. The description's statement that businessCategory must be one of TikTok's twenty-five published values merely restates the schema constraint. It adds little semantic depth, so a baseline of 3 is appropriate.

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 clearly states the tool returns industry averages for a TikTok business category in terms of specific metrics (likes, comments, shares, etc.), and explicitly distinguishes it from tiktok_account_insights by noting it provides cross-account averages, not the brand's own numbers. This makes the resource and scope unambiguous.

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?

It provides explicit usage guidance by pairing with tiktok_account_insights to answer comparative questions, and mentions a prerequisite (TikTok account authorization). It doesn't state explicit when-not-to-use conditions, but the pairing directive gives clear context for when this tool is the right choice.

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