datasets_pitchbook_advisors_facets

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

With no annotations provided, the description carries the full burden. It states the primary behavior (returns terms aggregation counts), which implies a read-only operation. However, it does not disclose any other behavioral traits such as default limits, pagination, or interaction between multiple filter parameters. The description is not misleading but could be richer.

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 two sentences long, front-loads the key purpose, and contains zero wasted words. Every sentence adds meaningful information about the tool's function and required facet values.

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 the 8-parameter schema with 100% coverage and no output schema, the description explains the core function (returns terms aggregation counts) and the required facet parameter. It does not explain response shape in detail, but the phrase 'terms aggregation counts' gives sufficient context for a facet endpoint. The tool appears complete for its purpose.

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 the baseline is 3. The description repeats the facet enum values which are already in the schema's facet parameter description. It does not add any new parameter semantics beyond what the schema provides, so a 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 states a specific verb and resource: 'Returns terms aggregation counts for the PitchBook advisors dataset.' It clearly distinguishes itself from sibling tools like search (datasets_pitchbook_advisors_search) and item (datasets_pitchbook_advisors_item) by focusing on faceted aggregation. Listing the facet enum (service_type, hq_country, hq_state, run_id) further specifies the exact scope.

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 usage: you call this tool when you need facet counts, and you must supply a facet from the listed enum. However, it does not explicitly state when to prefer this over search/item tools, nor does it mention any exclusions. It provides the required enum but lacks explicit alternatives or when-not-to-use guidance.

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