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

The description goes beyond the sparse annotations by explaining that the operation edits only the masked area and returns a link to the result. This is meaningful behavioral context, though it does not address processing time, credit costs, or whether the original image is left unchanged.

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?

Two short sentences carry a precise purpose, a behavioral detail, and the return type. Nothing is wasted, and the main action 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 3-parameter tool, the description plus schema covers inputs and output adequately: image, mask, prompt, and result link are all specified. It lacks only minor operational context such as costs or asynchronous behavior, which are not critical for invoking the tool.

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 covers 100% of parameters with clear descriptions (including mask color semantics and fal URL requirements), so the base score is 3. The tool description itself adds no parameter-level meaning beyond what the schema already provides.

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 uses a specific verb-resource pair ('Edits part of an image') and precisely defines the operation: 'repaints the masked area to match a prompt.' This clearly distinguishes inpainting from sibling tools like remove_background, upscale, and generate_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 the tool should be used for repainting masked image regions, but it does not state when to prefer it over alternatives or when not to use it. No exclusions or sibling comparisons are provided, so usage guidance is only implicit.

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