NanoBanana has 7 tools with clear action verbs and reasonable descriptions (avg 85 chars). All tools have input schemas with type definitions and parameter descriptions. However, output schemas are not documented, LLMs cannot predict what fields to expect from responses. Parameter descriptions lack format constraints (e.g., aspect_ratio enum values are listed in description but not formalized). Error handling is minimal; no recovery guidance or actionable error messages visible. Missing tool annotations (readOnlyHint, destructiveHint). Composition is sound, tools are single-purpose and chainable via URL references.

• Document output schemas for all tools. Specify: generate_image returns {image_url, generation_id, metadata}; analyze_image returns {description, tags, detailed_analysis}; etc. Include field types and whether results are paginated.
• Convert parameter constraints to formal enums/patterns in JSON Schema. For generate_image: style as enum [cinematic, product-photography, ...], aspect_ratio as enum [1:1, 2:3, ...], resolution as enum [1K, 2K, 4K]. Remove example values from descriptions.
• Add tool annotations: mark generate_image, edit_image, swap_background, create_variations with destructiveHint=true; mark analyze_image and list_styles with readOnlyHint=true. This signals to agents which tools have side effects.
• Add error handling guidance to tool descriptions. E.g., generate_image: 'If API quota exceeded, wait 60s and retry. If reference_images URL is invalid, call upload_image first to get a valid URL.'
• Add min/max constraints to numeric parameters in schema. variation_count: {type: integer, minimum: 1, maximum: 4}. This prevents LLMs from passing out-of-range values.