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JavaScript
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datasets_pitchbook_funds_facets

Get term aggregation counts for PitchBook funds by strategy, status, or run ID. Filter by query, vintage years, and exact fields to analyze fund distributions.

Instructions

Facet PitchBook funds dataset. Returns terms aggregation counts for the PitchBook funds dataset. Facet enum: fund_strategy, fund_status, run_id.

Input Schema

TableJSON 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

B3.4/5.0
Behavior3/5

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

With no annotations and no output schema, the description carries the transparency burden. It does disclose that this returns aggregation counts rather than records and restricts facets to three enums. It does not explain the output shape, pagination, or how optional filters like q and run_id affect the counts.

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?

The description is concise and front-loaded with the core behavior. The only flaw is repeating 'PitchBook funds dataset' in both sentences, a minor redundancy rather than a structural problem.

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

Completeness3/5

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

It is minimally adequate: an agent knows to supply one of the three facet fields and expects term-count aggregation results. But without an output schema, it omits useful context such as the exact shape of the returned counts and whether filters restrict the aggregation universe.

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 all parameters. The description only repeats the facet enum values already present in the schema and adds no deeper semantics about parameter combinations, formats, or constraints.

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 states a specific action ('Facet' / returns terms aggregation counts) and resource ('PitchBook funds dataset'), and lists the three supported facet fields. It does not explicitly name sibling tools, but the aggregation wording distinguishes it from search and item tools well enough.

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?

The return type ('terms aggregation counts') and facet enum imply the tool is for dataset faceting/browsing, so usage is somewhat clear. However, there is no explicit guidance contrasting it with datasets_pitchbook_funds_search or datasets_pitchbook_funds_item, nor any when-not-to-use conditions.

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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