job_result

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so behavioral safety is clear. The description adds that it returns 'normalized provider output' for validation jobs, but does not elaborate on important details like permissions or side effects beyond the annotations.

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, well-structured sentence that immediately conveys the core purpose and key differentiator. No extraneous information, making it highly efficient for an AI agent to parse.

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 has no output schema, and the description does not explain the return format or structure. While the input schema is fully described, the lack of output details reduces completeness. The description should clarify what 'normalized provider output' entails.

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 100%, with each parameter already having a description. The description does not add new information about parameters; it merely reinforces the context of 'validation job' and 'normalized output', which aligns with the schema but does not improve understanding of individual parameter semantics.

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 verb 'Collect' and the resource 'VALIDATION job's normalized provider output', effectively differentiating it from a sibling tool 'llm_job_result'. This makes the tool's purpose specific and unambiguous.

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 distinguishes this tool from llm_job_result by specifying it returns normalized output for validation jobs, while raw output is returned by the sibling. However, no guidance is given for other sibling tools, leaving some ambiguity in broader context.

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