datasets_pitchbook_investors_facets

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

With no annotations provided, the description carries the burden of disclosing behavior. It notes the output is terms aggregation counts and lists valid facet enums, but it does not describe the response structure, whether filters interact with faceting, or any operational constraints. Adequate but sparse.

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, front-loaded with purpose and key enum values. Every word earns its place; no filler or redundancy.

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 no output schema and no annotations, the description is incomplete. It fails to explain the structure of the returned aggregation counts, how filter parameters like q or run_id affect results, or any pagination/limits. A bare-minimum faceting tool would benefit from more detail on output shape and interaction with filters.

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 coverage is 100%, so the schema provides full parameter descriptions. The description adds no additional parameter semantics beyond repeating the facet enum, which is already in the schema. Baseline 3 is appropriate.

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 the tool 'Facet PitchBook investors dataset' and specifies it returns terms aggregation counts. This distinguishes it from sibling item and search tools for the same dataset, and the resource is explicitly named.

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 implies use for getting aggregated counts rather than individual records or searches, but it does not explicitly state when to prefer this over the sibling search/item tools or provide exclusions. Context is clear but not fully differentiated.

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