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LKbaba

grok-mcp

by LKbaba

grok_agent_search

Perform web and X (Twitter) searches with Grok AI, which analyzes queries, synthesizes findings, and returns cited answers for research and trend tracking.

Instructions

Intelligent search powered by Grok AI. Supports Web search, X (Twitter) search, or mixed search. Grok automatically analyzes queries, executes searches, synthesizes information, and provides cited answers. Ideal for getting latest information, researching topics, and tracking social media trends.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoGrok model to use: - grok-4.20-multi-agent-beta-0309: 4-Agent collaboration, lowest hallucination rate (default) - grok-4.20-beta-0309-reasoning: Deep chain-of-thought reasoning - grok-4.20-beta-0309-non-reasoning: Fastest speed, creative divergencegrok-4.20-multi-agent-beta-0309
queryYesSearch query content
search_typeNoSearch type: - web: Web search only - x: X (Twitter) search only - mixed: Both Web and X search (recommended)mixed
output_formatNoOutput format: - text: Markdown format (default) - json: Structured JSON (enforced via native JSON Schema)text
x_search_configNoX search config (when search_type is x or mixed). Image understanding is enabled by default.
web_search_configNoWeb search config (when search_type is web or mixed). Image understanding is enabled by default.
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the workflow: 'automatically analyzes queries, executes searches, synthesizes information, and provides cited answers.' This is meaningful behavioral context, though it omits any caveats or limitations that might be expected for a search tool.

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?

Three concise sentences that front-load the purpose, then describe capabilities and ideal use cases. Every sentence adds value with no redundancy or fluff.

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?

The description provides a solid overview for a complex tool with nested configs and no output schema. It explains the output as 'cited answers' and covers main capabilities. However, it does not address the detailed configuration options (e.g., date ranges, domain restrictions), relying on the schema for those.

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 baseline is 3. The description only echoes the search_type options (Web, X, mixed) and does not add new parameter semantics beyond what the schema already provides.

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 the tool's function: 'Intelligent search powered by Grok AI' with specific capabilities (Web, X, mixed search). It distinguishes itself from the sibling tool grok_brainstorm by focusing on search and information synthesis, not ideation.

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?

Provides context on when to use: 'Ideal for getting latest information, researching topics, and tracking social media trends.' This gives clear usage scenarios, though it does not explicitly mention when not to use or comparison to alternatives beyond the sibling's name.

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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