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datasets_pitchbook_investors_facets

Return facet aggregation counts for PitchBook investor records by status, investor type, headquarters country/state, or crawl run ID to analyze distribution.

Instructions

Facet PitchBook investors dataset. Returns terms aggregation counts for the PitchBook investors dataset. Facet enum: status, investor_type, hq_country, hq_state, run_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over name and description, max 256 characters
facetYesFacet enum: status, investor_type, hq_country, hq_state, run_id
run_idNoExact crawl run-id filter, max 128 characters
statusNoExact status filter, max 128 characters
hq_stateNoExact headquarters state/region filter, max 128 characters
hq_countryNoExact headquarters country filter, max 128 characters
investor_typeNoExact investor type filter, max 128 characters
min_exits_countNoMinimum number of exits
min_portfolio_countNoMinimum current portfolio size

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "status",
+  "investor_type",
+  "hq_country",
+  "hq_state",
+  "run_id"
+]
  2. Added

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It communicates the key trait that this returns aggregation counts rather than records, but it adds little beyond that: no mention of how the optional q/filter parameters constrain the aggregation, no pagination/limit context, and no output-shape details despite the absence of an output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short, front-loaded with the operation and resource, and contains no filler. It loses one point because 'PitchBook investors dataset' is repeated in consecutive sentences, which is mildly redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple facet call the description gives the core return type and the schema documents all nine parameters. It is not fully complete for agent selection because it never clarifies when to use facets versus search/item siblings, and it does not explain how the optional filters affect the aggregations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented with type, enum, and max-length context where relevant. The description merely repeats the facet enum; it adds no extra parameter meaning, so the high-coverage baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a concrete action ('Facet') and result ('Returns terms aggregation counts') on a specific resource, the PitchBook investors dataset. The facet enum list makes the scope concrete, and the count-based return type distinguishes it from the sibling 'search' and 'item' tools, though it does not explicitly name that distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to choose this tool over datasets_pitchbook_investors_search, datasets_pitchbook_investors_item, or other dataset facet tools. The wording implies a count/aggregation use case, but there are no explicit when-to-use, when-not-to-use, or alternative-selection cues.

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

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