wafle_ai_jobs_reject

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

With no annotations, the description carries the full burden. It explains the intended benefits but does not disclose side effects, reversibility, permission requirements, or what happens to the job/output. Mutation tools need more behavioral disclosure.

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?

Two sentences, front-loaded with the primary action, and no wasted words. Very concise and well-structured.

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?

Adequate for a simple rejection action, but gaps remain: no explanation of id/slug relationship, no mention of the 'reason' parameter, and no post-conditions. The description covers the main intent but lacks depth.

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 coverage is only 33% (slug has a description; id and reason do not). The description adds no parameter explanation, leaving id and reason ambiguous. With low coverage, the description should compensate but doesn't.

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 the action: 'Mark an AI job's output as rejected.' This is a specific verb and resource, and the name 'reject' distinguishes it from sibling tools like accept/edit/get.

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: 'Use to keep usage stats clean and to preserve learning signal for prompt tuning.' This explains when to use it, though it doesn't explicitly discuss alternatives or when not to use it.

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