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

With annotations only marking this as non-readonly, non-destructive and non-idempotent, the description carries substantial extra behavior: async painting (~30 s), that the first painting becomes the tree, the return payload (design_id and genome), and a hard rate limit counting rejected/interrupted attempts. This is exactly the beyond-annotations context an agent needs.

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?

Four tight sentences, front-loaded with purpose, then mechanism/timing, return values, limits, and a workflow guard. Every sentence earns its place with no filler.

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?

No output schema exists, yet the description still discloses what comes back (design_id, genome), how long the async step takes, the rate constraint, and the required post-call UX. For a one-parameter generator this leaves nothing material unaddressed.

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 the single 'wish' parameter with length and content constraints; the description reinforces meaning with a worked example. It adds illustrative value but no syntax or format detail beyond the schema, so it sits just above the baseline.

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 gives a specific verb+resource transformation ('Turns a short wish ... into a premium tree design') and immediately disambiguates from the sibling get_design by framing this as generation rather than retrieval. The concrete example ('a silver birch with lanterns') makes the expected input land instantly.

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?

It provides real workflow context: 'Show the result to the user before you order' steers the agent to the downstream ordering step, and the 3-attempts/day cap tells the agent not to retry blindly. It stops short of explicitly naming the sibling to call next (plant_tree / start_sponsorship), leaving that to inference.

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