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datasets_pitchbook_limited_partners_facets

Return value counts for PitchBook limited partners by institution type, HQ country/state, or run ID. Filter by query or founding year; top 50 values returned.

Instructions

Facet PitchBook limited partners dataset. Returns value counts for the PitchBook limited partners dataset by the required facet: institution_type, hq_country, hq_state or run_id. Filters q, min_year_founded and max_year_founded also apply, and the top 50 values are returned.

Input Schema

NameRequiredDescriptionDefault
qNoFull-text query over name and description, max 256 characters
facetYesFacet enum: institution_type, hq_country, hq_state, run_id
run_idNoExact crawl run-id filter, max 128 characters
hq_stateNoExact headquarters state/region filter, max 128 characters
hq_countryNoExact headquarters country filter, max 128 characters
institution_typeNoExact institution type filter, max 128 characters
max_year_foundedNoMaximum founding year
min_year_foundedNoMinimum founding year

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "institution_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 add real behavioral detail: it returns value counts and caps output at the top 50 values, which is non-obvious and useful. It stops short of stating the read-only nature or what happens to unlisted filters, but the return-shape disclosure is solid.

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 tight sentences that lead with the core action and then the return behavior and limit. No filler; every clause adds 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?

For an aggregation tool with no output schema, the description adequately covers what comes back (value counts, top 50). It is slightly incomplete on how the filters and facet interact and on routing versus the sibling search/item tools.

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 the baseline is 3. The description clarifies that facet is required and is the grouping key and that the three named filters apply, but it adds little beyond the enum and descriptions already in the schema.

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 limited partners dataset') and explains the output is value counts grouped by the required facet. This distinguishes it functionally from the sibling search/item tools as an aggregation endpoint, though it never names those siblings 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?

It implies usage by stating the facet is required and that q/min_year_founded/max_year_founded filters apply, giving some operational context. However, it never says when to use this facet tool versus datasets_pitchbook_limited_partners_search or _item, leaving the routing decision to inference.

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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