set_data_validation

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

Given no annotations, the description partially discloses behavior: it can clear validation by passing null rule_type, and dry_run returns an execution plan. However, it does not mention permissions, destructiveness, or response for normal execution.

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 a single opening line followed by a concise, well-organized bullet list of arguments with clear explanations. No redundant or unnecessary information.

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?

Although parameter details are thorough, the description omits return value (except for dry_run) and does not mention error conditions, prerequisites, or side effects. For a tool with 8 parameters and no output schema, this leaves gaps in understanding.

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?

With 0% schema description coverage, the description adds full meaning for all 8 parameters, including examples and allowed values (e.g., rule_type: ONE_OF_LIST, NUMBER_BETWEEN). This compensates entirely for the missing schema descriptions.

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 uses specific verbs 'Set (or clear)' and resource 'data validation on a range', clearly distinguishing from sibling tools like clear_range or set_conditional_format.

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?

No explicit guidance on when to use this tool vs alternatives, nor mention of prerequisites or exclusions. The description only states what the tool does, not when to apply it.

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