Veezee: LinkedIn people & company data for agents

Search X (formerly Twitter)

x_search

Read-only

Search X by keywords. type picks the mode: recent (default) is the deep chronological sweep and keeps paginating as far as you follow the cursor; popular returns the highest-engagement tweets for the query; people finds accounts and returns a single page (no cursor). Advanced query operators pass through verbatim, e.g. "from:nasa", "min_faves:100", exact phrases in quotes -- there is no separate date parameter, so use since:/until: operators for time windows. Costs 6 credits per page; a cursor page is a NEW call priced the same way, so a deep sweep costs linearly in pages. Returns tweet summaries (text, author with follower count, views, likes, retweets, replies, created_at, url, id) with a cursor. Fetch one tweet's full detail with x_get_tweet and an account's timeline with x_get_tweets. For comment-level sentiment inside topic communities, reddit_search type=comments is usually the sharper instrument.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`type`	No	recent = chronological deep sweep; popular = top engagement; people = account search (single page).	recent
`query`	Yes	Free-text keywords; X advanced operators work, e.g. 'claude code from:AnthropicAI since:2026-06-01'.
`cursor`	No	Cursor from a previous page. Not valid with type 'people'.
`freshness`	No	recent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data). Trial keys are cached-only and reject realtime with TRIAL_CAP_EXCEEDED; paying upgrades this same key to unlock it.	recent
`max_credits`	No	Spend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`usage`	Yes
`common`	Yes
`entity`	Yes
`platform`	Yes
`freshness`	Yes
`data_as_of`	Yes
`canonical_url`	Yes
`schema_version`	Yes
`platform_fields`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedSep 10, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Discloses cost per page (6 credits), pagination behavior (cursor page is a new call), freshness parameter implications (cached vs realtime, trial key limitations), lack of date parameter, and return structure (tweet summaries with cursor). No contradiction with readOnlyHint/openWorldHint.

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?

Single paragraph with all necessary information, front-loaded with core purpose. Somewhat dense but well-organized; could be slightly restructured for even easier scanning.

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

Completeness5/5

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

Covers all aspects: purpose, usage, cost, parameters, return format, sibling differentiation, and limitations. Output schema exists but description still summarizes return fields adequately. No gaps for an agent to use correctly.

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

Parameters5/5

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

Schema coverage is 100%, but description adds major value: explains behavioral differences of type enum, cursor incompatibility with 'people', freshness cost dynamics, and advanced operator usage with examples. Greatly enhances understanding beyond schema.

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?

Description starts with 'Search X by keywords' and elaborates on three distinct modes (recent, popular, people) with specific behaviors. It distinguishes from sibling tools like x_get_tweet and x_get_tweets, making purpose unambiguous.

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

Usage Guidelines5/5

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

Explicitly tells when to use each mode: recent for deep chronological sweep, popular for top engagement, people for account search. Provides alternatives: fetch single tweet with x_get_tweet, timeline with x_get_tweets, and mentions reddit_search for comment-level sentiment.

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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