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JavaScript
Remote

datasets_bbb_businesses_facets

Get distribution counts for BBB businesses by category, state, city, rating, accreditation, entity type, or run ID, applying the same filters as business search.

Instructions

Facet the BBB businesses dataset. Returns distribution counts over the BBB businesses index (dataset id enum value bbb-businesses), honoring the same filters as search. Facet enum: category, state, city, rating, accredited, entity_type, run_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text match on the business name/category, max 256 characters
cityNoExact city filter
facetYesFacet enum: category, state, city, rating, accredited, entity_type, run_id
stateNoExact 2-letter state/province filter
ratingNoExact letter-grade rating filter. Enum: A+, A, A-, B+, B, B-, C+, C, C-, D+, D, D-, F
run_idNoExact crawl run id filter
categoryNoExact category filter
accreditedNoAccreditation filter
entity_typeNoExact entity-type filter

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "category",
+  "state",
+  "city",
+  "rating",
+  "accredited",
+  "entity_type",
+  "run_id"
+]
    • addedInput schema / properties / rating / enum
      Added value: +[
+  "A+",
+  "A",
+  "A-",
+  "B+",
+  "B",
+  "B-",
+  "C+",
+  "C",
+  "C-",
+  "D+",
+  "D",
+  "D-",
+  "F"
+]
  2. Addedv1.16.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden. It discloses that it returns distribution counts over the index and honors search filters, which are key behaviors. However, it does not disclose potential limitations such as result size, pagination, or the exact output format beyond 'distribution counts,' leaving some behavioral uncertainty.

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 two sentences with no unnecessary words, and the key action ('Facet... Returns distribution counts') is front-loaded. It lists the facet enum compactly and references search filters, achieving high information density.

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?

The tool has 9 parameters and no output schema, yet the description covers the main behavioral aspect (distribution counts) and links filters to search. It does not explain output structure, how multiple filters are combined, or any limits, so for a complex tool it is not fully complete. A 3 reflects that it is adequate but has gaps.

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 adds the relationship 'honoring the same filters as search,' which provides cross-tool context, and lists the facet enum (also in schema). This is marginal added value over the schema, so a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Facet'), identifies the exact resource ('BBB businesses dataset' with dataset id `bbb-businesses`), and differentiates from sibling search/item tools by stating it returns distribution counts and honors the same filters as search. This makes the tool's purpose distinct and clear.

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 description implies the tool is for getting distribution counts and mentions it honors search filters, but it does not explicitly state when to use this versus the sibling search or item tools, nor does it name alternatives or give exclusions. Usage context is present but left 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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