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datasets_creators_facets

Discover creator dataset facet values by niche, country, or verified status using search filters; returns up to 50 count-ordered buckets and excludes private/deleted accounts unless inactive included.

Instructions

Discover values in the creators dataset. Returns up to 50 creator-facet buckets ordered by count, using the same filters as datasets_creators_search. Page and page_size do not change the aggregation. Deleted/private accounts remain excluded unless include_inactive=true. Contact-email values are never returned. Facet enum: niche, country, verified.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "facet": "<facet>"
    },
    "name": "datasets_creators_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Full-text query over handle, nickname and bio
`page`	No	Result page, 1-based
`sort`	No	Validated search sort; bucket order stays count-desc, and time-series date_desc keeps its search history scope
`facet`	Yes	Facet field
`niche`	No	Exact content niche
`handle`	No	Exact creator handle
`country`	No	Exact creator country/region
`verified`	No	Verified badge filter
`has_email`	No	Contact-email presence filter
`page_size`	No	Page size, default 20 and max 100
`min_followers`	No	Minimum follower count
`include_inactive`	No	Include deleted/private accounts

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the burden well: it discloses the 50-bucket cap, count-desc ordering, that page/page_size do not affect the aggregation, the include_inactive exclusion rule, and that contact-email values are suppressed. Gaps remain around permissions/authentication requirements and whether counts are approximate, which for an unannotated tool keeps it from a 5.

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?

Front-loaded with what the tool returns, then behavioral caveats, then the facet enum — every sentence carries information. The closing facet enum duplicates the schema's enum and its per-property description, which is the one mildly wasteful element.

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 12-parameter, unannotated, no-output-schema tool the description covers the important surprises: bucket cap, ordering, pagination irrelevance, inactive-account policy, and email suppression. It could go further on auth requirements or how to consume the bucket counts, but nothing an agent needs to invoke it correctly appears missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds genuine meaning beyond the schema: page and page_size have no effect on the aggregation, and include_inactive governs whether deleted/private accounts appear in buckets. It also constrains facet to the three enum values, though that repeats the schema's enum.

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?

States a specific verb and resource ('Discover values in the creators dataset') plus the return unit ('creator-facet buckets'), and explicitly names the sibling search tool whose filter set it mirrors. An agent can distinguish it from datasets_creators_search without opening either schema.

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

Usage Guidelines4/5

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

The reference to 'the same filters as datasets_creators_search' tells the agent this is the aggregation counterpart to the record-listing tool, which is strong contextual routing. It stops short of an explicit when-to-use/when-not statement (e.g. 'use search to retrieve records, use this to count them'), so it is clear context rather than full 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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