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

The description goes beyond the annotations by explaining the tool's non-destructive, non-final nature: it returns an editable JSON document that the UI renders in an editor for the user to rearrange before saving. It also discloses the copy discipline (fewer layers = cleaner layout) which is a behavioral trait. However, it does not explicitly state side effects (e.g., whether it persists anything) but the description implies no saving occurs. Given annotations already provide readOnlyHint=false and destructiveHint=false, the description adds meaningful context without contradiction, earning a 4.

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 (core description, USE FOR, DIFFERENT FROM, COPY DISCIPLINE, RETURNS). It is front-loaded with the most important information (what it returns and how it differs from the flat alternative). Every section adds value and no redundant sentences exist. The length is justified by the tool's complexity and the need to differentiate from a sibling.

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?

For a tool with 10 parameters and a complex output structure, the description is remarkably complete. It covers the output format (JSON layered document), the workflow (UI renders for editing), the copy discipline, and the exact use cases and exclusions. Since the schema fully documents all parameters, the description's guidance on when to pass which copy parameters completes the picture. An agent can call this tool correctly with the information provided, even without an output schema.

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 schema already documents each parameter. The description adds extra value by providing guidance on parameter usage: 'Default to headline + cta (add subheadline only when it genuinely earns its place); pass body / disclaimer ONLY when the user explicitly asked for that copy.' This gives semantic guidance beyond the schema, particularly for optional copy parameters. It doesn't repeat schema text, so it earns a 4.

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's specific purpose: generating an editable, layered brand creative with a clean AI background and editable text/CTA/logo layers, returning a structured layout document (JSON) rather than a flat image. It also distinguishes itself from the sibling generate_brand_creative by explicitly stating the difference (baked text vs. editable layers, flat PNG vs. document). This leaves no ambiguity about what the tool does and how it differs from a close alternative.

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 'USE FOR' section explicitly enumerates when to use this tool (default for standalone creative requests, any request where the user should edit/tweak) and the 'DIFFERENT FROM' section names the exact alternative (generate_brand_creative) and the conditions for choosing that instead (building an ad inline or explicitly requesting a finished flat image). This is explicit when/when-not guidance with a named sibling, which fully resolves selection ambiguity.

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