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

The description discloses cost ($0.03 per image), asynchronous execution, and the return/polling contract (generation_id, status 'completed'). This goes well beyond the annotations, which only indicate that the tool is not read-only and operates in an open world.

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?

Three short sentences each carry distinct information: purpose, pricing, and async behavior. There is no redundancy and the most critical information is front-loaded.

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 simple two-parameter tool with no output schema, the description provides the essential return identifier and polling instructions. An agent has everything needed to invoke the tool and retrieve the result.

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%, so the schema already documents both parameters and their formats. The description adds no extra parameter-specific meaning beyond reiterating that the tool takes an image and a prompt.

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 names a specific verb ('edit'), resource ('photos'), and method ('with a prompt'), making the tool's purpose unambiguous. It also distinguishes this from image generation tools like ai_image_generator, and the async note clarifies that the output is a generation_id, not an immediate edited image.

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 implies when to use this tool: when a photo should be edited via a prompt. It gives a concrete follow-up workflow (call get_generation until completed) but does not explicitly contrast with sibling tools or state when an alternative should be preferred.

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