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

Annotations only declare readOnlyHint=false and destructiveHint=false, leaving the description to add real behavioral context. It does so well: credit consumption, a one-week free re-query window, per-source reporting, and the crucial null-versus-empty-list distinction. This is meaningful operational detail beyond 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?

The description is front-loaded with what the tool does, then when to use it, then cost and reporting semantics. Every sentence carries useful information with no filler, and the null/empty-list clarification is placed where an agent will see it before interpreting results.

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?

Given no output schema, the description compensates by describing returned fields, source-by-source reporting, and what null versus empty means. For a credit-consuming research tool with a single required parameter, this is complete enough for correct invocation and interpretation.

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% and the single domain parameter is documented in the schema with an example. The description lists returned fields rather than adding input semantics beyond what the schema already provides, so the baseline of 3 applies.

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 exactly what the tool returns: currently running ads for a domain from Meta Ad Library and Google Ads Transparency Center, with specific field types. It clearly distinguishes this from broader sibling tools like research_competitors by naming the two ad sources and the exact output.

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 gives explicit trigger conditions: what a competitor is advertising, whether they run paid ads, how long an ad has been live, or wanting hooks and angles. It also notes credit cost and free re-query caching. It does not name alternative sibling tools for adjacent queries, 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.