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Pantainos-Toolkit

grok-mcp

search_x

Read-only

Search X posts using Grok to get a summarised answer with citations, synthesised from relevant tweets.

Instructions

Search X (Twitter) posts using Grok and return a summarised answer with citations.

The xAI server autonomously searches X, analyses results, and synthesises an answer grounded in real posts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language search query (e.g. "What are people saying about $TSLA?").
to_dateNoOnly include posts on or before this date (ISO 8601).
from_dateNoOnly include posts on or after this date (ISO 8601, e.g. "2026-02-01").
temperatureNoSampling temperature (0-2).
system_promptNoOptional system prompt to shape the response style.
allowed_handlesNoWhitelist of X usernames to restrict search to (max 10).
excluded_handlesNoX usernames to exclude from results (max 10).
enable_video_understandingNoLet the model analyse video clips in posts.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description adds valuable behavioral context beyond the readOnlyHint and openWorldHint annotations: it discloses that the tool operates autonomously, searches, analyzes, and synthesizes answers grounded in real posts, and includes citations. This informs the agent about the tool's internal behavior, though it does not disclose potential limitations like rate limits or authentication requirements.

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

Conciseness5/5

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

The description is compact—two sentences—and front-loads the core purpose. Every sentence adds meaningful information: the first states the main function, the second explains the autonomous synthetic process. No unnecessary filler or repetition.

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?

With an output schema available, the description does not need to explain return values, and it already covers the essential purpose, process, and output type ('summarised answer with citations'). It could include more on limitations or prerequisites, but for a search tool with rich schema and annotations, it is adequately complete.

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?

The input schema already provides 100% coverage with detailed descriptions for all 8 parameters (e.g., query, date filters, temperature, allowed/excluded handles). The tool description itself does not add parameter-level information, so it stays at the baseline 3 without adding or requiring compensation.

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?

The description clearly states a specific action ('Search X (Twitter) posts') and the resource (X posts) using Grok, with a distinct outcome ('return a summarised answer with citations'). It also explains the autonomous process, making the tool's purpose unmistakable even in the absence of sibling tools.

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 for querying X posts and receiving a synthesized answer, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. With no sibling tools, the lack is less critical, but the absence of exclusions or context (e.g., 'use for real-time social media analysis') keeps this at a mid score.

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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