datasets_facebook_pages_facets

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

There are no annotations, so the description carries the burden of behavioral disclosure. It does disclose the core behavior: returns terms aggregation counts. But it doesn't describe the response shape, any limits on returned buckets, or how optional filters affect aggregation. No contradiction with annotations exists.

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 the operation and return type front-loaded. The only minor redundancy is repeating 'Facebook Pages dataset' twice, but there is no filler or irrelevant content.

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?

With 15 parameters, no annotations, and no output schema, the description is minimal. It confirms aggregation counts and the facet enum, and the schema covers parameters, so an agent can likely invoke it correctly. But it doesn't explain whether the many filters can constrain the aggregation, any default limits, or output structure, leaving meaningful gaps for a complex tool.

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?

The input schema has 100% description coverage across all 15 parameters, so the schema already documents each one. The description adds no new parameter meaning beyond restating the facet enum that is already in the schema, which matches the baseline of 3.

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 a specific verb ('facet') with a clear resource ('Facebook Pages dataset') and states the exact output: terms aggregation counts. It also enumerates the valid facet values, making it easy to distinguish from sibling tools like datasets_facebook_pages_search and datasets_facebook_pages_item.

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?

The description clearly conveys that this tool is for getting facet counts on the Facebook Pages dataset, and the required 'facet' parameter makes the use case evident. However, it does not explicitly name alternatives or state when not to use this tool versus the search or item siblings, so it lacks exclusion guidance.

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