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datasets_pitchbook_companies_facets

Generate value counts for PitchBook companies by required facet (status, industry, financing, ownership, HQ, run ID), filtered by query, year, and investor count; returns top 50.

Instructions

Facet PitchBook companies dataset. Returns value counts for the PitchBook companies dataset by the required facet: status, primary_industry, financing_status, ownership_status, hq_country, hq_state or run_id. Filters such as q, min_year_founded and min_investor_count apply, and the top 50 values come back.

Input Schema

NameRequiredDescriptionDefault
qNoFull-text query over name and description, max 256 characters
facetYesFacet enum: status, primary_industry, financing_status, ownership_status, 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
financing_statusNoExact financing status filter, max 128 characters
max_year_foundedNoMaximum founding year
min_year_foundedNoMinimum founding year
ownership_statusNoExact ownership status filter, max 128 characters
primary_industryNoExact primary industry filter, max 128 characters
min_investor_countNoMinimum number of investors

Schema Changelog

Changes observed during successful MCP inspections.

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

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations the description carries the full burden, and it discloses two key behaviors: the tool returns value counts (not records) and the result is capped at the top 50 values. It omits details like whether counts respect all filters or how ties are broken, but the truncation disclosure is genuinely valuable.

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?

Front-loads the purpose, then facet options, then applicable filters, then return behavior, with no filler sentences. Slightly dense but every clause carries information.

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

Completeness4/5

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

With no annotations and no output schema, the description supplies the essential missing pieces: that output is value counts and that it is truncated to 50 values. Given the 12-param schema is fully described, this is nearly complete, missing only tie-breaking/pagination nuance.

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 all 12 parameters are already documented in the schema. The description names the facet enum and a few filters, adding no syntax or format detail beyond the schema, so baseline 3 applies.

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

Purpose5/5

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

States a specific verb ('Facet') and resource ('PitchBook companies dataset') and explains that it returns value counts. The facet values are enumerated, and the 'facets' vs 'search'/'item' sibling distinction is clear from the operation described.

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

Usage Guidelines3/5

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

It notes which filters 'apply' but never states when to choose this facets tool over the sibling datasets_pitchbook_companies_search. Usage is only implied by the value-count semantics rather than explicitly framed.

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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