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

Annotations declare readOnlyHint=true and openWorldHint=true, so the description needn't state safety. It adds valuable context about the facet-application behavior and the relationship to the search tool's query filters. It doesn't cover pagination behavior beyond what the schema says, but given the annotations the bar is lower.

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 short sentences with zero waste, front-loading what the tool returns and then the usage note. Appropriately sized for a facet tool.

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?

An output schema exists, so the description needn't explain return values. The description provides the key context—that it returns aggregation counts and should be used with the search tool—without over-explaining. A bit more on how facets interact with pagination would help, but it's sufficient.

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 schema already documents all 20 parameters in detail. The description doesn't add parameter-level syntax or meaning beyond noting that facets are applied 'under search filters.' Baseline 3 is appropriate when 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 clearly states a specific verb+resource: 'Returns terms aggregation counts for the Facebook Pages dataset under search filters.' This distinguishes it from sibling datasets_facebook_pages_search and _item, though it doesn't explicitly contrast with them.

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 says 'Use alongside the related search tool to inspect filter counts under the same query filters,' which implies when to use it relative to the search tool. However, it doesn't name the sibling tool explicitly or describe when NOT to use it.

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