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datasets_x_users_search

Search X (Twitter) user profiles with filters for followers, verification, bio, and date range. Sort by relevance or follower count.

Instructions

Search the X users dataset. Searches public X (Twitter) user profiles stored in a search index. Sort enum: relevance, followers_desc, followers_asc, crawled_at_desc, crawled_at_asc, created_at_desc, created_at_asc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over username, name, bio and location, max 256 characters
pageNoPage number, defaults to 1
sortNoSort enum: relevance, followers_desc, followers_asc, crawled_at_desc, crawled_at_asc, created_at_desc, created_at_asc
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 (low values surface follow-spam / bot-like accounts)
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
source_tierNoExact filter for which seed tier discovered this account, e.g. github-users, wikidata, tiktok-creators, journalists
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
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions searching a search index but omits details like pagination behavior, rate limits, authentication, or output format. The sort options are listed but already in schema.

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?

The description is relatively concise at two sentences. It front-loads the main action and lists sort options efficiently, though it could be slightly more structured with bullet points or sections.

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 17 parameters and no output schema, the description is too minimal. It does not explain pagination constraints, result format, or how the search index works. The sibling tool names suggest a related item tool exists, but this connection is not clarified.

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?

All 17 parameters have schema descriptions (100% coverage), so the description adds minimal new meaning. The description redundantly lists sort enum values. Baseline score of 3 is appropriate.

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 states it searches the X users dataset and searches public X user profiles. However, it does not explicitly differentiate from sibling tools like datasets_x_users_facets or datasets_x_users_item, relying on the name to indicate search functionality.

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 guidance on when to use this tool versus alternatives such as datasets_x_users_facets or datasets_x_users_item. The description implies general search usage but lacks explicit context for choosing this tool.

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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