grok_validate

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

No annotations are provided, so the description carries the full burden. It adequately describes outcomes (scored scorecard, identifies weaknesses, returns improved version) and mentions the default model. It could be more explicit about side effects (e.g., does not modify the original artifact), but overall it provides sufficient behavioral disclosure for a non-destructive analysis tool.

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 extremely concise: three sentences covering function, outputs, and usage recommendation. It front-loads the core action and avoids any fluff. Every sentence earns its place.

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?

Given there is no output schema, the description adequately explains return value (scored scorecard, identified weaknesses, improved version). With 5 parameters (1 required) and no nested objects, the description covers the essential context. It could benefit from mentioning the output format or that the artifact is not mutated, but it is sufficient for an AI agent.

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 100%, so the schema already documents all 5 parameters thoroughly. The description adds no additional semantic detail beyond the schema (e.g., it doesn't elaborate on valid values for 'rounds' or 'rubric'), making it merely adequate. The default model mention is not a 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 states it runs a validation protocol on any artifact, produces a scorecard, identifies weaknesses, and returns an improved version. The verb 'validate' and resource 'artifact' are specific, and it distinguishes from siblings like 'ask_grok' (Q&A) and 'generate_image' (image generation).

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 explicitly recommends use as a 'mandatory quality gate before shipping complex work,' providing clear when-to-use guidance. It does not explicitly state when not to use, but the sibling tool list (ask_grok, generate_image, etc.) implies alternatives. The default model mention adds useful context.

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