datasets_pitchbook_limited_partners_facets

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

With no annotations, the description carries the full burden of behavioral disclosure. It states that the response contains 'terms aggregation counts', which implies a read-only operation, but it does not explain the response structure, pagination, limits, or how filters like q and run_id interact with facets. The description lacks crucial behavioral details for an agent to reliably parse and use the result.

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 core action, and lists the facet enum efficiently. Every sentence serves a clear purpose, and there is no wasted text.

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?

The description is minimal for a tool with 8 parameters and no output schema. It does not explain the return format of 'terms aggregation counts' (e.g., buckets with keys and doc_count), nor how the facet tool relates to the search tool for exploration workflows. An agent would struggle to know what to expect in the response without external knowledge of typical Elasticsearch/OpenSearch facets.

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?

The schema covers 100% of parameter descriptions, so the baseline is 3. The description adds no meaning beyond repeating the facet enum, which is already present in the schema. It does not explain how parameters like q, run_id, or year filters influence the facet counts, though the schema already defines these filters.

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 returns terms aggregation counts for the PitchBook limited partners dataset, distinguishing it from sibling search/item tools which return records. The facet enum is explicitly listed, leaving no ambiguity about the tool's purpose.

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 the tool is for retrieving aggregation counts rather than raw data, but it does not explicitly state when to use this tool versus the sibling search or item tools. No exclusions or alternative usage guidance is provided, so the agent must infer context from the tool name and sibling names.

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