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datasets_pitchbook_advisors_facets

Break down PitchBook advisors by service type, headquarters country/state, or crawl run, returning top 50 counts that respect search and founding-year filters.

Instructions

Facet PitchBook advisors dataset. Returns value counts for the PitchBook advisors dataset by the required facet: service_type, hq_country, hq_state or run_id. The counts honour the search filters, including q, min_year_founded and max_year_founded, and list the top 50 values.

Input Schema

NameRequiredDescriptionDefault
qNoFull-text query over name and description, max 256 characters
facetYesFacet enum: service_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
service_typeNoExact service provider 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: +[
+  "service_type",
+  "hq_country",
+  "hq_state",
+  "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, the description carries the full burden. It usefully discloses that results are value counts, that they respect filters, and that output is capped at the top 50 values - a genuine behavioral trait. But it omits anything about whether facets ignore certain filters, pagination, or the shape of the response, so gaps remain.

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?

Two sentences, front-loaded with the core action and the required parameter, with the filter behavior and top-50 cap following. Slight redundancy with the schema's facet enum, but no filler.

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 an 8-parameter tool with no annotations and no output schema, the description covers the return nature (value counts, top 50) but leaves ambiguity about how filter params interact with the chosen facet (e.g., can you filter by the same dimension you facet on) and says nothing about the response structure.

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 only restates the facet enum values and a few filter names that the schema already documents, adding no syntax or constraint detail beyond what structured data provides.

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+resource ('Facet PitchBook advisors dataset. Returns value counts...') and enumerates the facet values, so it is clearly distinguishable from the search and item siblings by behavior. It stops short of naming a sibling to route against, which keeps it from a 5.

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 the use case (aggregate distributions gated by the same search filters as the query tools) by listing q, min_year_founded, max_year_founded as honored filters. However it never explicitly states when to pick this over datasets_pitchbook_advisors_search, nor any when-not guidance.

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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