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datasets_pitchbook_limited_partners_search

Search PitchBook limited partner profiles by name, institution type, HQ country/state, or founding year to find pension funds, endowments, and insurance investors.

Instructions

Search PitchBook limited partners dataset. Searches the crawled public PitchBook limited partner (institutional investor — e.g. pension fund, endowment, insurance company) profile catalog stored in a search index. Discovered from PitchBook's public sitemap. Some limited partner profiles have no FAQ section -- this is normal, not a sign of missing data. Sort enum: relevance, name_asc, year_founded_desc, recently_crawled_desc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over name and description, max 256 characters
pageNoPage number, defaults to 1
sortNoSort enum: relevance, name_asc, year_founded_desc, recently_crawled_desc
run_idNoExact crawl run-id filter, max 128 characters
hq_stateNoExact headquarters state/region filter, max 128 characters
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
hq_countryNoExact headquarters country filter, max 128 characters
institution_typeNoExact institution type filter (e.g. Corporate Pension, Private Investment Fund, Endowment), 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 / sort / enum
      Added value: +[
+  "relevance",
+  "name_asc",
+  "year_founded_desc",
+  "recently_crawled_desc"
+]
  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, the description carries full responsibility for behavioral disclosure. It does add valuable context: the data is crawled from PitchBook's public sitemap, and profiles missing FAQ sections are normal rather than a data defect. It does not, however, disclose response format, pagination behavior beyond schema defaults, or any rate-limit/auth considerations, which are significant given the absence of annotations.

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 three sentences with no filler. Purpose and data source are front-loaded, and the FAQ note is a worthwhile behavioral signal. The sort-enum listing is somewhat redundant with the schema, but it is concise and does not bloat the description.

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?

For a search tool with 10 optional parameters, no annotations, and no output schema, the description gives reasonable context: what the dataset is, where it came from, and one data-quality expectation. It does not describe the shape of returned records, how the search results relate to the item/facets siblings, or constraints beyond what the schema already states. It is adequate but not complete for a tool with this complexity.

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 and the schema already documents all 10 parameters. The description re-lists the sort enum values and gives example investor types, but these largely duplicate the schema's own descriptions. It adds no meaningful new semantics about how the filters interact or how to combine them.

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 opens with a specific verb and resource: 'Search PitchBook limited partners dataset.' It also clarifies the dataset scope (institutional investor profile catalog in a search index, discovered from PitchBook's public sitemap), which helps an agent understand what it searches. However, it does not explicitly distinguish itself from sibling tools like datasets_pitchbook_limited_partners_item or datasets_pitchbook_limited_partners_facets, relying on the name to carry that differentiation.

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 usage context is implied: this is the 'search' tool over the limited partners catalog, so an agent can infer it is for full-text/filtered lookup. It even provides a data-quality note (missing FAQ sections are normal) that helps set expectations. Yet it never states when to prefer this tool over the facets or item siblings, and there are no exclusions or alternative routing clues.

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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