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datasets_facebook_pages_facets

Returns value counts for the Facebook Pages dataset by category or discovery source, with filters like search, likes, and contact info to narrow results.

Instructions

Facet the Facebook Pages dataset. Returns value counts for the Facebook Pages dataset, by the required facet: category or discovery_source. Search filters such as q, min_likes and has_email narrow the counts. Default order is most common first; order=value_asc pages alphabetically using after and next_after, with limit up to 200.

Input Schema

NameRequiredDescriptionDefault
qNoFull-text query over title and address, max 256 characters
sortNoSort enum: relevance, likes_desc, likes_asc, hydrated_at_desc, hydrated_at_asc
afterNoContinuation from next_after; requires order=value_asc and unchanged filters
facetYesFacet enum: category, discovery_source
limitNoFacet values per response, default 50, maximum 200
orderNocount_desc returns most common values; value_asc pages through all values
page_idNoExact Facebook Page id filter, max 128 characters
categoryNoExact Page category filter (case-insensitive), max 128 characters
has_emailNoFilter by a public contact email
has_phoneNoFilter by at least one public phone number
max_likesNoMaximum Page like count
min_likesNoMinimum Page like count
identifierNoExact Page username/identifier filter (case-insensitive), max 128 characters
has_websiteNoFilter by a linked website
has_whatsappNoFilter by a public WhatsApp contact
hydrated_afterNoRecords last refreshed on or after this date (RFC3339 or YYYY-MM-DD)
hydrated_beforeNoRecords last refreshed on or before this date (RFC3339 or YYYY-MM-DD)
discovery_sourceNoExact filter for how the Page was discovered (e.g. business_search, warc_domain_scan, wikidata), max 128 characters

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv1.17.5
    • addedInput schema / properties / after
      Added value: +{
+  "description": "Continuation from next_after; requires order=value_asc and unchanged filters",
+  "type": "string"
+}
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "category",
+  "discovery_source"
+]
    • addedInput schema / properties / limit
      Added value: +{
+  "description": "Facet values per response, default 50, maximum 200",
+  "type": "integer"
+}
    • addedInput schema / properties / order
      Added value: +{
+  "description": "count_desc returns most common values; value_asc pages through all values",
+  "enum": [
+    "count_desc",
+    "value_asc"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "relevance",
+  "likes_desc",
+  "likes_asc",
+  "hydrated_at_desc",
+  "hydrated_at_asc"
+]
  2. Addedv1.16.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses genuinely useful traits: the default ordering (most common first), the `order=value_asc` alternative, the cursor mechanism via `after`/`next_after`, and the 200 limit. It omits anything about permissions, whether the operation is read-only, or rate behavior, so it only partially meets the higher no-annotation bar.

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 dense sentences with no filler, front-loaded with the core purpose ('Facet ... Returns value counts') before filters and ordering details. Slightly packed but every clause contributes.

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 an 18-parameter aggregation tool with no output schema, the description covers the essential mechanics: the required facet, that filters narrow counts, and the ordering/pagination model. It is largely complete, with only the sibling routing (facet vs search/item) left implicit.

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 all 18 parameters are already documented in the schema, establishing a baseline of 3. The description adds little beyond the schema, echoing `facet`, a few filter names, `order`, `after`/`next_after`, and `limit` without new syntax 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?

States a specific verb ('Facet') and resource ('the Facebook Pages dataset') and clarifies the return is value counts by the required `facet`. An agent can distinguish this aggregation tool from a plain search, though the search sibling (datasets_facebook_pages_search) is never named for contrast.

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 'facet ... returns value counts' framing implies the tool is for aggregation/counting, and it notes that search filters narrow the counts. However, it never states explicitly when to use this versus datasets_facebook_pages_search or datasets_facebook_pages_item, leaving an agent to infer the routing.

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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