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datasets_pitchbook_funds_facets

Count PitchBook funds by fund strategy, status, or run ID with optional query, vintage year, and other filters to analyze dataset distributions.

Instructions

Facet PitchBook funds dataset. Returns value counts for the PitchBook funds dataset by the required facet: fund_strategy, fund_status or run_id. The search filters q, fund_strategy, fund_status, min_vintage_year and max_vintage_year apply to the counts, which list the top 50 values.

Input Schema

NameRequiredDescriptionDefault
qNoFull-text query over name and description, max 256 characters
facetYesFacet enum: fund_strategy, fund_status, run_id
run_idNoExact crawl run-id filter, max 128 characters
fund_statusNoExact fund status filter, max 128 characters
fund_strategyNoExact fund strategy filter, max 128 characters
max_vintage_yearNoMaximum vintage year
min_vintage_yearNoMinimum vintage year

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "fund_strategy",
+  "fund_status",
+  "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 burden well by disclosing the return behavior: value counts grouped by the chosen facet, limited to the top 50 values, with search filters affecting the counts. It stops short of describing permissions, ordering, or pagination behavior, but the core behavior is clear.

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?

The description is front-loaded and compact, with every sentence contributing: first the operation, then the returned counts, then the filtering behavior and limit. There is no wasted text.

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 a seven-parameter facet tool with full schema coverage and no output schema, the description is largely complete: it explains the required facet, the returned counts, and the top-50 limit. It slightly under-documents the run_id parameter as a filter, since run_id is mentioned only as a facet option.

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 schema already documents the parameters. The description adds useful context by stating that the listed filters affect the facet counts, but it does not meaningfully elaborate on parameter formats or semantics beyond 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?

The description gives a specific verb and resource: it facets the PitchBook funds dataset and returns value counts grouped by a required facet. It clearly distinguishes the output as counts rather than records, though it does not explicitly name the sibling search/item tools it contrasts with.

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 by describing the tool as a facet/count operation and listing which search filters apply. However, it does not explicitly say when to choose this over datasets_pitchbook_funds_search or datasets_pitchbook_funds_item.

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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