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datasets_pitchbook_investors_facets

Count PitchBook investors by status, type, country, state, or run ID. Apply query, portfolio, and exit filters to see top 50 facet values.

Instructions

Facet PitchBook investors dataset. Returns value counts for the PitchBook investors dataset by the required facet: status, investor_type, hq_country, hq_state or run_id. The counts follow search filters including q, min_portfolio_count and min_exits_count, and show the top 50 values.

Input 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

A3.8/5.0
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 does disclose useful behavior beyond the schema: counts are affected by the same search filters, and only the top 50 values are returned (a result-size limit the agent should know). It omits auth/rate-limit and read-only confirmation, so not a 5.

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 core action and resource, then the return shape. Every clause earns its place with no redundancy.

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?

There is no output schema, so the description must carry return semantics, and it does: value counts keyed by facet, top 50 only, honoring search filters. It is sufficient for correct invocation, though it doesn't describe how filters combine or how the value counts are ranked.

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 coverage is 100%, so parameters already document the enum values, filter fields, and length limits. The description names facet values and a few filters but adds no syntax or semantics beyond the schema, so the 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?

States a specific verb ('facet') and resource ('PitchBook investors dataset') and clarifies the output is value counts by a required facet field. It implicitly distinguishes itself from datasets_pitchbook_investors_search (which returns records) by describing counts, but never names or contrasts the sibling explicitly.

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?

Usage is implied - the tool counts values under search filters, which suggests it is for facet/distribution exploration - but there is no explicit when-to-use guidance or named alternative (search vs item vs facets). Nothing is misleading; the agent must infer.

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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