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datasets_pitchbook_companies_facets

Get counts for PitchBook companies facets including status, industry, financing, ownership, and location to analyze data distribution.

Instructions

Facet PitchBook companies dataset. Returns terms aggregation counts for the PitchBook companies dataset. Facet enum: status, primary_industry, financing_status, ownership_status, hq_country, hq_state, run_id.

Input Schema

TableJSON 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

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that it returns counts, but does not mention read-only nature, authentication needs, rate limits, or how filters interact. There is no mention of side effects or return format beyond 'counts', which is minimal.

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

Conciseness5/5

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

Two sentences, front-loaded with the tool's purpose and the facet enum list. No wasted words or repetition. The structure is efficient and easy to scan.

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

Completeness2/5

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

For a tool with 12 parameters and no output schema, the description is too minimal. It does not explain the return structure beyond 'counts', does not mention how filters like q or run_id affect results, and does not note that it complements search tools. Missing context for correct invocation.

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 parameters have descriptions in the schema. The description lists the facet enum, which is redundant with the schema. It does not add meaning about how parameters combine (e.g., q with facet) or any syntax details. Baseline 3 is appropriate given the high schema coverage.

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 clearly states the tool returns terms aggregation counts for the PitchBook companies dataset, identifying the verb and resource. It distinguishes from search and item tools by mentioning 'counts', but does not explicitly contrast with sibling facet tools like datasets_pitchbook_advisors_facets. Still, the dataset is named, so the purpose is specific.

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?

There is no guidance on when to use this tool versus alternatives like datasets_pitchbook_companies_search or item. It does not mention that it is for facet exploration before a search, or any exclusions. The description is purely functional and leaves usage decisions to the agent.

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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