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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds nothing behavioral — no note on how the optional filters constrain the aggregation population, or that results are counts rather than records. With annotations carrying the safety load, this is a minimal addition.

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?

A single tight sentence with the resource front-loaded and zero filler. It is efficient, though the extreme brevity leaves room for a clause about what facet aggregation actually returns.

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 and all parameters are documented in the schema, so return values and inputs are covered. What is missing is the conceptual link between the filters and the aggregation (i.e., that filters scope the facet counts), which an agent would otherwise have to infer.

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% across all 11 parameters, so the schema fully documents q, facet, filters, and pagination. The description adds no meaning beyond that, which is the baseline 3 when the schema does all the work.

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?

Names a specific operation (facet aggregation) against a specific dataset (PitchBook limited partners), which separates it from the dataset's _item and _search siblings. It stops short of explicitly contrasting with those siblings, so it is clear but not fully differentiating.

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 gives no when-to-use guidance, no mention of when facet aggregation is preferable to datasets_pitchbook_limited_partners_search, and no prerequisites. Usage must be inferred entirely from the name and operation type.

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