Editar imagen (Nano Banana)

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

No annotations are provided, so the description must carry the full burden. It does not disclose whether the original image is modified in place, what the return value is, or any side effects. It only lists edit types and image input formats, which is more about purpose and parameters than behavioral transparency.

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 two sentences, front-loaded with the core action ('Edita una imagen existente según un prompt') and immediately followed by concrete examples. Every sentence earns its place with no redundancy.

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?

The description covers what the tool does and input formats, but omits what the tool returns (presumably the edited image) and any behavioral caveats. Given the tool's moderate complexity and lack of output schema, this is a notable gap.

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 already covers both parameters fully (100% coverage). The description adds value by providing examples of edit types ('inpainting, cambio de estilo, retoques'), which clarifies the expected prompt semantics beyond the schema's generic 'Instrucción de edición.'

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 edits an existing image based on a prompt, listing specific use cases (inpainting, style change, retouching). It distinguishes from siblings: generate_image creates new images, compose_images combines images, while edit_image modifies an existing one.

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 you have an existing image and want to modify it via a prompt. However, it does not explicitly mention alternatives or exclusions, though the context is clear enough for an agent to decide.

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