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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description usefully adds the contract that it 'honors the same filters as search,' but says nothing about why a facet tool exposes page/page_size or how its counts are scoped.

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 sentences, no filler, with the output shape stated first and the usage note second. Nothing is padded or repeated.

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 an output schema present, return values need not be described, and the 12 fully documented parameters are the schema's job. The definition covers what the tool returns and how filters relate to search, leaving only minor gaps around pagination behavior for a count-style 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?

Schema description coverage is 100%, so every parameter including facet's allowed values is already documented. The description only restates 'one field' and 'same filters as search', adding no syntax or semantics beyond the schema; baseline 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 a specific verb and resource: 'returns terms-aggregation counts for one field over the PitchBook investors dataset.' That is enough to separate it from the item/search siblings on the same dataset, though it refers to the sibling only as 'the related search tool' rather than naming it.

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 one clear usage context ('use alongside the related search tool to inspect filter counts under the same query filters'), which implies the facet-count-before-filtering workflow. There is no when-not guidance and no explicit rule for when to prefer the search or item tool over this one.

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