Apply Feedback to Draft

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

The description discloses useful behavioral traits beyond the annotations: it runs asynchronously, returns a pending status, and saves feedback history. These details align with the annotations (readOnlyHint=false, openWorldHint=true) and add context for the agent without contradicting the structured metadata.

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 well-structured with clear sections for model selection, args, and returns. It front-loads the core purpose and keeps information focused; the parameter list slightly duplicates the schema but is not overly verbose.

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 tool's moderate complexity (4 params, background execution), the description covers key aspects: return values, model options, API key handling, and side effects. The existence of an output schema further reduces the need for detailed return explanations, making this description sufficient for correct invocation.

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?

The schema already provides descriptions for all parameters (100% coverage), but the description adds value by explaining model selection trade-offs (flash vs pro) and the API key fallback behavior. This goes beyond the schema's simple parameter labels and descriptions.

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 that the tool applies user feedback to a draft and runs in the background. It distinguishes itself from sibling tools like blog_apply_review_feedback by focusing on 'user feedback' on drafts, though it does not explicitly differentiate from the pro variant.

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 context about background execution and feedback history but gives no explicit guidance on when to choose this tool over alternatives such as blog_apply_feedback_pro. No conditions, exclusions, or comparative recommendations are offered.

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