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

The description goes well beyond the annotations (which only set readOnlyHint=false and destructiveHint=false) by detailing the job submission, polling, R2 folder copy, URL writing, credit cost, and timeout handling. No contradiction with 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 extremely concise: two sentences, with the main action in the first sentence and process details in the second. Every sentence adds value, no fluff or repetition.

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 the complexity (8 parameters, 3 required, output schema present), the description covers all key aspects: the action, the multi-step process, cost implications, timeout behavior, and fallback. It is complete for an AI agent to understand how and when to invoke the tool.

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%, so the baseline is 3. The description explains the overall workflow but does not add significant meaning beyond the schema for individual parameters. For example, it mentions modelCode from list_image_models but provides no additional syntax or constraints.

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 tool generates an AI image and attaches it as a post's featuredImage, specifying the verb and resource. It distinguishes from siblings like attach_post_cover_from_job and list_image_models by mentioning them, providing a specific scope.

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 explicit context: it costs credits (reference to list_image_models), describes polling timeout behavior, and suggests using attach_post_cover_from_job as a fallback. While it doesn't explicitly state when not to use, it offers sufficient guidance for correct invocation.

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