tiktok_validate_query

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

With no annotations provided, the description carries the full disclosure burden. It conveys that the tool is non-executing ('BEFORE executing'), that it only checks compatibility rules, and it lists the three validation categories. It does not disclose return format, error/failure behavior, or rate limits, which would strengthen it, but the core behavioral profile — validate, don't execute — is clearly conveyed.

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?

Two sentences with no filler: the first front-loads the purpose and timing, the second lists the validation scope. Every clause contributes information, and the tool name is not merely restated.

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?

The defnition is adequate for a validation tool but has real gaps: no output schema exists, yet the description never says what the tool returns (e.g., boolean pass/fail, list of violations, or thrown error), so an agent cannot know how to interpret the result before proceeding. With no annotations and two undocumented parameters, some of that burden shifts to the description, and it does not fully carry it.

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 only 50%, with dataLevel and queryLifetime lacking schema descriptions. The description partially compensates by linking 'lifetime compatibility' to queryLifetime and 'metric/dimension combination' to the metrics/dimensions arrays, but it never addresses dataLevel's semantics or why the chosen level matters for validation. The added meaning over the schema is modest.

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 states a specific verb ('Validate'), a resource ('TikTok metric/dimension combination'), and the pre-execution role ('BEFORE executing'). It further enumerates the exact checks performed (dimension grouping rules, lifetime compatibility, metric/dimension compatibility), which clearly differentiates it from the many tiktok_get_* siblings that execute queries.

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?

'BEFORE executing' gives clear contextual guidance on when this tool is needed — as a pre-flight guard before query tools like tiktok_get_insights or tiktok_get_report_raw. However, it does not explicitly name alternative tools or state when-not-to-use scenarios, so it stops short of a 5.

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