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datasets_youtube_creators_facets

Aggregate YouTube creator data by region or discovery source to get term counts for faceted analysis. Filter channels by query, followers, views, videos, bio, links, and dates.

Instructions

Facet the YouTube creators dataset. Returns terms aggregation counts for the YouTube creators dataset. Facet enum: region, discovery_source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over channel_name and bio, max 256 characters
sortNoSort enum: relevance, followers_desc, followers_asc, views_desc, videos_desc, hydrated_at_desc, hydrated_at_asc
facetYesFacet enum: region, discovery_source
regionNoExact channel region/country filter (case-insensitive), max 128 characters
has_bioNoFilter by a non-empty About bio
has_linksNoFilter by at least one linked external URL
max_viewsNoMaximum total view count
min_viewsNoMinimum total view count
channel_idNoExact channel id filter, max 128 characters
max_videosNoMaximum uploaded-video count
min_videosNoMinimum uploaded-video count
joined_afterNoChannels created on or after this date (RFC3339 or YYYY-MM-DD)
joined_beforeNoChannels created on or before this date (RFC3339 or YYYY-MM-DD)
max_followersNoMaximum subscriber count
min_followersNoMinimum subscriber count
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 channel was discovered, max 128 characters
views_count_availableNoFilter by whether the channel has a known total view count
videos_count_availableNoFilter by whether the channel has a known uploaded-video count
followers_count_availableNoFilter by whether the channel exposes a public subscriber count

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "region",
+  "discovery_source"
+]
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "relevance",
+  "followers_desc",
+  "followers_asc",
+  "views_desc",
+  "videos_desc",
+  "hydrated_at_desc",
+  "hydrated_at_asc"
+]
  2. Addedv1.16.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the key output behavior ('Returns terms aggregation counts') and the allowed facet enum, which is useful. But it does not state that the operation is read-only, that filter parameters can be combined with the facet, or what the response structure looks like. This is minimum-viable disclosure.

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 with a clear front-loaded verb and no filler, but the dataset name is repeated twice ('YouTube creators dataset' in both sentences), which is mildly redundant. Otherwise efficient.

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 tool with 21 parameters, no output schema, and no annotations, the description is sparse. It explains what the tool returns in broad terms but not how it relates to the sibling search tool, whether filters narrow the aggregation, or the shape of the aggregation response. The schema covers parameters, but the description does not complete the operational picture.

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 repeats the facet enum values already present in the schema without adding any additional parameter semantics, such as whether filters narrow the aggregation or how the q parameter interacts with faceting.

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 verb ('Facet') and resource ('the YouTube creators dataset'), and adds the output type ('Returns terms aggregation counts'), which clearly separates it from the document-returning sibling datasets_youtube_creators_search. However, it does not explicitly name or contrast any sibling, so it stops short of full differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or when-not-to-use guidance. The description implies usage by stating it facets the dataset, but it does not mention that it should be used when aggregate counts per region or discovery_source are needed instead of records, nor does it reference the sibling search tool. An agent must infer the decision.

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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