Edit Image with Nano Banana

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

The description adds valuable behavioral context beyond what annotations provide. While annotations indicate read-only and non-destructive operations, the description elaborates on what the model actually does (add/remove/modify elements, change style/lighting/colors, adjust composition, apply filters), maintains original style/context, and includes error handling details. This provides practical behavioral insight that annotations alone don't convey.

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 well-structured with clear sections (purpose, capabilities, args, returns, examples, error handling) and front-loads the core functionality. While comprehensive, some sections like the detailed parameter documentation could be more concise given the schema already covers them thoroughly. Most sentences earn their place by adding value.

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?

Given the tool's complexity (image editing with AI models), the description provides excellent contextual completeness. It covers purpose, capabilities, parameters, return values, examples, and error handling. While there's no output schema, the 'Returns' section thoroughly documents the response structure. The combination of rich description and comprehensive annotations makes this highly complete.

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?

With 100% schema description coverage, the schema already thoroughly documents all parameters. The description's 'Args' section essentially repeats what's in the schema without adding significant additional semantic context. The baseline score of 3 is appropriate since the schema does the heavy lifting, though the description provides some clarification about default values and model capabilities.

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's purpose with specific verb ('Edit') and resource ('an existing image'), and distinguishes it from siblings by specifying it's for editing existing images (vs. generating new ones with nanobanana_generate_image or composing multiple images with nanobanana_compose_images). The opening sentence establishes this distinction immediately.

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 provides clear context for when to use this tool ('Edit an existing image using text prompts') and includes examples that illustrate appropriate use cases. However, it doesn't explicitly state when NOT to use it or mention specific alternatives among the sibling tools, which prevents a perfect score.

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