grok_query

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

With no annotations, the description carries the full burden and does well: it discloses live web search, xAI Responses API usage, inline citations vs. sources_considered, the x_search toggle, and the default model. It omits rate limits, auth requirements, and cost/latency characteristics, but the core read-only query behavior is clear.

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 compact and front-loaded: purpose and return shape come first, followed by concise behavioral details. No sentence is filler.

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?

An output schema exists, so return values need not be fully documented. However, with no annotations and 0% schema parameter coverage, the description remains incomplete for a four-parameter tool because system_prompt and model semantics are not explained.

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?

Schema description coverage is 0%, so the description must compensate. It explains x_search and the default model, and the purpose implies the question parameter, but it does not explain system_prompt or describe valid model values.

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 states a specific verb and resource: ask Grok a question and return an answer with cited URLs. The provider name distinguishes it from sibling LLM query tools such as openai_query, claude_query, and perplexity_query, though it does not explicitly compare itself to them.

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?

Usage is implied by the provider-specific name and the live web search capability, but the description does not say when to choose this over perplexity_query, openai_query, or multi_llm_query, nor does it state exclusions.

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