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datasets_youtube_creators_facets

Returns value counts for YouTube creators by region or discovery source, honoring subscriber, video, view, and date filters to reveal top 50 segment distributions.

Instructions

Facet the YouTube creators dataset. Returns value counts for the YouTube creators dataset by the required facet: region or discovery_source. The counts honour the search filters, such as subscriber, video and view ranges and join or refresh dates, and cover the top 50 values.

Input 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

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does meaningful work: it discloses that output is aggregated value counts, that the facet is required and limited to region/discovery_source, that filters are applied to the counts, and that results are capped at the top 50 values. Remaining gaps (pagination beyond top 50, ordering of counts) are modest.

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?

Three tight sentences with the core action and the required facet front-loaded. Minor redundancy in repeating 'the YouTube creators dataset' twice, but nothing extraneous.

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 a 21-parameter aggregation tool with no output schema and no annotations, the description covers the essentials an agent needs: what is returned, what facet keys are allowed, that filters apply, and the top-50 cap. Return-field structure and count ordering are unstated but low-risk.

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 21 parameters, and the description's restatement of the facet enum adds nothing new. It does marginally help by grouping the filter parameters into thematic buckets ('subscriber, video and view ranges and join or refresh dates'), but that is not new information beyond the schema.

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 and resource ('Facet the YouTube creators dataset') and clarifies the return shape ('value counts ... by the required `facet`'). It is clearly distinguishable from a plain search tool, but it never names the sibling search/item tools, so differentiation is implicit rather than explicit.

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 phrase 'the counts honour the search filters' implies this tool is the aggregation complement to datasets_youtube_creators_search, but there is no explicit when-to-use/when-not statement or named alternative. Usage is inferable rather than stated.

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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