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

The annotations already mark this as read-only and non-destructive, and the description adds meaningful behavior beyond that: every recipe is backed by at least five distinct advertisers, sub-threshold recipes are suppressed, and an empty result means 'not enough proven data' rather than 'no data exists'. It even instructs the agent to report this plainly instead of guessing, which is high-value behavioral disclosure.

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 longer than average but every section earns its place: definition, when-to-use, when-not-to-use with named alternatives, and empty-result semantics. It is front-loaded with the core purpose and uses clear paragraph breaks and capitalization to aid scanning.

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 read-only search tool with an output schema, the description covers purpose, selection criteria, exclusions, and the meaning of empty results. Nothing needed to call the tool correctly is missing, and the output schema handles return-value details.

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 100%, so the structured schema already documents every parameter and even gives query examples. The description reinforces the semantic theme by listing channel, ad format, audience type, bid strategy, and budget band as the dimensions of a recipe, but it does not add per-parameter detail beyond what the schema provides. Baseline 3 is appropriate because the schema does the heavy lifting.

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 names a specific verb and resource ('Find proven CAMPAIGN CONFIGURATIONS to build from') and immediately clarifies the scope: combinations of channel, ad format, audience type, bid strategy, and budget band. It explicitly distinguishes itself from sibling analytics tools, so an agent can tell it apart from query_metadata_analytics_benchmarks and query_metadata_analytics_account without opening their schemas.

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 USE THIS WHEN guidance tied to composing campaigns and deciding how to build them, plus a crisp framing: answers 'what should I build', not 'how is my account performing'. It names the exact sibling tools for the cases it should NOT be used for, leaving no ambiguity about routing.

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