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datasets_x_users_facets

Count X users by facet filters such as blue-verified status, bio presence, external URL, or source tier to quantify profile segments within the dataset.

Instructions

Facet the X users dataset. Returns terms aggregation counts for the X users dataset. Facet enum: is_blue_verified, has_bio, has_external_url, source_tier.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over username, name, bio and location, max 256 characters
sortNoSort enum: relevance, followers_desc, followers_asc, crawled_at_desc, crawled_at_asc, created_at_desc, created_at_asc
facetYesFacet enum: is_blue_verified, has_bio, has_external_url, source_tier
has_bioNoFilter by a non-empty profile bio
usernameNoExact username filter (case-insensitive), max 128 characters
max_ratioNoMaximum follower-to-following ratio
min_ratioNoMinimum follower-to-following ratio
source_tierNoExact filter for which seed tier discovered this account
crawled_afterNoRecords last refreshed on or after this date (RFC3339 or YYYY-MM-DD)
created_afterNoAccounts created on or after this date (RFC3339 or YYYY-MM-DD)
max_followersNoMaximum follower count
min_followersNoMinimum follower count
crawled_beforeNoRecords last refreshed on or before this date (RFC3339 or YYYY-MM-DD)
created_beforeNoAccounts created on or before this date (RFC3339 or YYYY-MM-DD)
has_external_urlNoFilter by a linked external URL
is_blue_verifiedNoFilter by the X blue-check verification flag

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "is_blue_verified",
+  "has_bio",
+  "has_external_url",
+  "source_tier"
+]
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "relevance",
+  "followers_desc",
+  "followers_asc",
+  "crawled_at_desc",
+  "crawled_at_asc",
+  "created_at_desc",
+  "created_at_asc"
+]
  2. Addedv1.6.0

TDQS

B3/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that terms aggregation counts are returned; it does not mention whether filters in the schema apply to the facet computation, whether multiple facets can be requested, or what the response structure looks like.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded, but it repeats 'X users dataset' twice in two sentences. The first sentence and the second sentence could be merged without losing information, making it slightly redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 16 parameters, no output schema, and no annotations, the description is too thin. It does not explain how the many filter parameters interact with the facet counts, nor what the returned aggregation structure looks like, leaving an agent to guess important call semantics.

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 16 parameters. The description adds the facet enum values, but those are also present in the schema, so it provides no meaningful parameter semantics beyond the baseline.

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 clearly names the resource ('X users dataset') and the operation ('Facet'), and explicitly states the output as 'terms aggregation counts'. This distinguishes it from sibling tools like datasets_x_users_search and datasets_x_users_item, though it does not name those alternatives directly.

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 usage: call this when you need facet/aggregation counts over the X users dataset. However, it gives no explicit guidance on when to prefer this over datasets_x_users_search or datasets_x_users_item, and no when-not-to-use conditions.

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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