Grok AI insights

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

There are no annotations provided, so the description is the sole source of behavioral disclosure. The description states the tool 'queries' Grok and returns structured insights, implying a read-only operation, but it does not disclose potential rate limits, authentication requirements, or the exact structure/format of the output. It adds minimal behavioral context beyond the basic action.

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

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

The description is a single, concise sentence that gets straight to the point with the verb 'Query' leading. It avoids unnecessary detail and is easy to scan.

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

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

The tool is simple (one parameter, no output schema), but the description leaves some gaps: 'structured AI-generated insights' is vague about what the output looks like. Given no annotations and no output schema, a bit more detail about the return format or use cases would enhance completeness. Still, the core function is understandable.

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

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

The schema already describes the only parameter ('query') as 'Brand name or query topic' with 100% coverage. The description's phrase 'about a brand or topic' essentially restates the schema, adding no additional meaning or usage details. The baseline of 3 is appropriate since the schema fully documents the parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's function with a specific verb ('Query') and resource ('Grok'), and states it produces structured AI-generated insights for a brand or topic. This distinguishes it from sibling tools like web_data_chatgpt_ai_insights and web_data_perplexity_ai_insights by naming the specific AI model.

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

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

The description provides clear context on what to use the tool for (querying Grok about a brand/topic), but it does not explicitly mention alternatives or when not to use it. Since the name and description make the Grok-specific nature clear, it serves as adequate guidance but lacks explicit exclusions or alternative tool references.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.