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AWS Nova Canvas

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by awslabs

generate_image_with_colors

Create images from text prompts with specific color palettes using Amazon Nova Canvas. Generate visuals for mockups, diagrams, and design concepts by providing descriptive text and hexadecimal color values.

Instructions

Generate an image using Amazon Nova Canvas with color guidance.

This tool uses Amazon Nova Canvas to generate images based on a text prompt and color palette.
The generated image will be saved to a file and the path will be returned.

IMPORTANT FOR Assistant: Always send the current workspace directory when calling this tool!
The workspace_dir parameter should be set to the directory where the user is currently working
so that images are saved to a location accessible to the user.

## Prompt Best Practices

An effective prompt often includes short descriptions of:
1. The subject
2. The environment
3. (optional) The position or pose of the subject
4. (optional) Lighting description
5. (optional) Camera position/framing
6. (optional) The visual style or medium ("photo", "illustration", "painting", etc.)

Do not use negation words like "no", "not", "without" in your prompt. Instead, use the
negative_prompt parameter to specify what you don't want in the image.

## Example Colors

- ["#FF5733", "#33FF57", "#3357FF"] - A vibrant color scheme with red, green, and blue
- ["#000000", "#FFFFFF"] - A high contrast black and white scheme
- ["#FFD700", "#B87333"] - A gold and bronze color scheme

Returns:
    McpImageGenerationResponse: A response containing the generated image paths.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe text description of the image to generate (1-1024 characters)
colorsYesList of up to 10 hexadecimal color values (e.g., "#FF9800")
negative_promptNoText to define what not to include in the image (1-1024 characters)
filenameNoThe name of the file to save the image to (without extension)
widthNoThe width of the generated image (320-4096, divisible by 16)
heightNoThe height of the generated image (320-4096, divisible by 16)
qualityNoThe quality of the generated image ("standard" or "premium")standard
cfg_scaleNoHow strongly the image adheres to the prompt (1.1-10.0)
seedNoSeed for generation (0-858,993,459)
number_of_imagesNoThe number of images to generate (1-5)
workspace_dirNoThe current workspace directory where the image should be saved. CRITICAL: Assistant must always provide this parameter to save images to the user's current project.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathsYes
statusYes
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool generates and saves images, returns file paths, and requires workspace_dir for accessibility. It also mentions prompt best practices and example color schemes. However, it doesn't cover potential limitations like rate limits, error conditions, or performance characteristics, which would be helpful for a complex tool with 11 parameters.

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 well-structured with clear sections (purpose, important note, best practices, examples, returns) and is appropriately sized for a complex tool. However, it includes some redundancy (e.g., repeating the importance of workspace_dir) and could be more front-loaded by moving critical usage instructions earlier. Every sentence adds value, but minor trimming could improve efficiency.

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

Completeness5/5

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

Given the tool's complexity (11 parameters, no annotations, but with output schema), the description is complete enough. It covers purpose, usage, behavioral context, and examples, and the output schema handles return values. The description compensates for the lack of annotations by providing practical guidance and warnings, making it sufficient for an agent to use the tool effectively.

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?

The schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description adds minimal parameter-specific semantics beyond the schema—it emphasizes the importance of workspace_dir and provides example color values, but doesn't explain the meaning or interaction of parameters like cfg_scale, seed, or quality. Given the high schema coverage, a baseline score of 3 is appropriate as the description adds some value but relies heavily on 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: 'Generate an image using Amazon Nova Canvas with color guidance.' It specifies the verb ('generate'), resource ('image'), and technology ('Amazon Nova Canvas'), and distinguishes it from the sibling tool 'generate_image' by emphasizing color guidance. The description goes beyond the name to explain the core functionality.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidelines, including when to use this tool (for image generation with color guidance) and critical instructions like 'Always send the current workspace directory when calling this tool!' It also offers prompt best practices and distinguishes from alternatives by noting the sibling tool 'generate_image' exists, though it doesn't explicitly say when to choose one over the other. The guidance is comprehensive and actionable.

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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