datasets_x_users_facets

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

No annotations exist, so the description must fully disclose behavior. It states it returns aggregation counts but does not mention that the numerous optional filter parameters (e.g., q, has_bio, min_followers) can restrict the dataset before aggregation. This omission reduces transparency for agents expecting predictable behavior.

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 concise with three sentences. However, it could be more structured by front-loading that it supports optional filters and then specifying the aggregation behavior. Minor room for improvement.

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 16 parameters (1 required, 15 optional) and no output schema, the description should explain what the aggregation output looks like and how optional filters affect it. The current description only mentions the facet enum, leaving agents to infer behavior from the schema, which is insufficient for complete 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?

Schema description coverage is 100%, so baseline is 3. The description lists the facet enum values (already in schema) but adds no additional meaning about how other parameters interact with the aggregation. No added value beyond schema.

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 it facets the X users dataset and returns terms aggregation counts for specified facet fields (is_blue_verified, has_bio, has_external_url, source_tier). It distinguishes from sibling tools like datasets_x_users_search (searching) and datasets_x_users_item (single item retrieval) by indicating aggregation behavior.

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 when-to-use or when-not-to-use guidance is provided. The description implies use for aggregated counts by facet fields, but does not mention that for detailed or filtered user lists, search should be used instead. Sibling names suggest differentiation, but the description itself lacks direct guidance.

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