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Process Nano Banana Queue

nanobanana_process_queue

Process queued prompt files to generate images using Gemini models, with validation, dry-run, and overwrite options for batch image creation.

Instructions

Process all prompt files in the queue directory and generate images.

Modes:

  • validate_only=true: Only validate prompts, no API calls

  • dry_run=true: Show what would be generated, no API calls

  • Both false: Actually generate images

Overwrite Strategies:

  • skip: Skip if output file exists (default)

  • overwrite: Replace existing files

  • rename: Generate with suffix (e.g., hero_1.png)

After successful generation, prompt files are moved to completed_dir with timestamp.

Example:

queue_dir: "nanobanana/queue"
dry_run: true
overwrite: "skip"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queue_dirNoDirectory containing prompt markdown filesnanobanana/queue
output_dirNoDefault directory to save generated images (used if prompt has relative path)assets/generated
completed_dirNoDirectory to move processed prompts tonanobanana/completed
modelNoGemini model to use for all generations (overrides prompt settings)gemini-2.5-flash-image
validate_onlyNoIf true, only validate prompt files without generating images
dry_runNoIf true, show what would be generated without actually calling API
overwriteNoStrategy when output file exists: skip, overwrite, or rename with suffixskip
Behavior4/5

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

The description adds valuable behavioral context beyond annotations: it explains file movement ('prompt files are moved to completed_dir with timestamp'), operational modes, and overwrite strategies. Annotations cover basic hints (e.g., not read-only, not destructive), but the description enriches this with practical details like no API calls in certain modes. No contradictions with annotations exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose, followed by organized sections for modes, overwrite strategies, and an example. Every sentence adds value, with no wasted words, making it efficient and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (batch processing with multiple modes and file operations) and lack of an output schema, the description does a good job covering behavior, parameters, and usage. However, it could improve by mentioning error handling or output format details, which would be needed for full completeness in this context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema already documents all parameters thoroughly. The description adds minimal extra semantics, such as clarifying that 'output_dir' is used 'if prompt has relative path' and providing an example with parameter values. This meets the baseline for high schema coverage but doesn't significantly enhance understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Process all prompt files in the queue directory and generate images.' This specifies the verb ('process'), resource ('prompt files'), and outcome ('generate images'), distinguishing it from sibling tools like 'nanobanana_generate_image' (single generation) and 'nanobanana_list_queue' (listing only).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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 the tool by detailing three operational modes (validate_only, dry_run, full generation) and overwrite strategies. However, it does not explicitly state when to use this batch processing tool versus the sibling 'nanobanana_generate_image' for single generations, which would be needed for a perfect score.

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

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