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agnes_image

Generate images from text prompts or edit existing images with text instructions. Supports multiple image inputs for composition and transformation.

Instructions

Capability 2 — Image generation & editing. Text-to-Image and Image-to-Image (edit/transform/multi-image compose). Models: agnes-image-2.1-flash, agnes-image-2.0-flash. Required: model, prompt, size. For image-to-image, pass 'image' (array of public URLs or data URIs). Note: image dimensions must be multiples of 16. Put response_format inside extra_body (handled automatically here).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoImage model name.agnes-image-2.1-flash
promptYesText prompt describing the desired image or edit instruction.
sizeNoOutput size, e.g. 1024x1024, 1024x768, 768x1024. Must be multiples of 16.1024x1024
imageNoInput images (public URL or data:image/...;base64,... URI) for image-to-image / multi-image.
response_formatNoOutput format. url (default) or b64_json.url
return_base64NoText-to-image only: top-level flag to return base64 (alternative to response_format=b64_json).
extra_bodyNoFree-form advanced parameters merged into extra_body.
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses dimension constraints and automatic handling of response_format, but does not cover error behavior, rate limits, or destructive nature of edits.

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

Conciseness4/5

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

The description is concise and front-loaded with purpose. It lists models and requirements efficiently, though it could be more structured with sub-sections.

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

Completeness3/5

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

Given 7 parameters and no output schema or annotations, the description covers input usage and constraints well, but lacks details on return structure and error scenarios, leaving some gaps for the agent.

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

Parameters4/5

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

Schema description coverage is 100%, but the description adds value by highlighting required parameters (model, prompt, size) even though model and size have defaults, and by explaining the image parameter usage for image-to-image and extra_body handling.

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 'Image generation & editing' and specifies Text-to-Image and Image-to-Image capabilities, distinguishing it from sibling tools like agnes_chat (text) and agnes_video_* (video). The verb 'generate' and 'edit' with resource 'image' is specific.

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?

It lists required fields (model, prompt, size) and explains when to include the 'image' parameter for image-to-image. However, it does not explicitly state when not to use this tool or compare to alternatives beyond sibling domains.

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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