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

Annotations declare a non-read-only, open-world, non-idempotent operation, and the description adds the crucial cost disclosure: 'Uses one House AI design credit.' That is real information not present in the structured fields. It does not say what the result is (rendered image vs. saved design) or whether it appears in list_my_designs, so it stops short of 5.

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?

One tightly packed sentence that front-loads the action, the input, the outcome, and the cost. No filler 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?

For a 6-parameter, nested-object generation tool with full schema coverage and annotations, the description covers purpose and cost well. The remaining gap is the return behavior — whether an image or a saved design comes back — which matters for a credit-consuming generation tool with no 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 description coverage is 100%, with per-field descriptions for color, photo, space, photo_url, room_type, and photo_upload_id, so the schema already carries parameter meaning. The description only reinforces the color phrasing ('sage green', 'warm white with black trim'), which is marginal added value over the schema's own example.

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?

States a concrete verb and resource ('repaint the walls in a photo of a room or house with a new paint color') plus the intent ('preview a color before painting'). An agent can tell this is a targeted recolor rather than a full redesign, though it never names how it differs from siblings like redesign_room or redesign_exterior.

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 phrase 'to preview a color before painting' implies the use case (try a color on an existing photo rather than restyle the room), and example colors show expected phrasing. However, there is no explicit when-to-use/when-not guidance and no named alternative such as redesign_room for broader changes, leaving the routing to inference.

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