AWS Nova Canvas MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PORT | No | Port number for the server | 8000 |
| AWS_REGION | No | AWS Region | us-east-1 |
| IMAGES_DIR | No | Path where generated images will be saved (default: nova_canvas_images folder in the user's home directory) | |
| BEDROCK_MODEL_ID | No | Model ID to use | amazon.nova-canvas-v1:0 |
| AWS_ACCESS_KEY_ID | Yes | AWS Access Key ID | |
| AWS_SECRET_ACCESS_KEY | Yes | AWS Secret Key |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| text_to_imageA | |
| inpaintingB | |
| outpaintingB | |
| image_variationB | |
| image_conditioningA | |
| color_guided_generationB | |
| background_removalC | |
| show_imageB | |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 8 tools
Each tool has a clearly distinct purpose within the image processing/generation domain. Background removal, color-guided generation, image conditioning, image variation, inpainting, outpainting, show_image, and text_to_image all target specific, non-overlapping operations. The descriptions clearly differentiate their functions, making tool selection unambiguous.
All tool names follow a consistent snake_case pattern with clear verb_noun or noun_verb structures (e.g., background_removal, color_guided_generation, show_image). The naming is predictable and readable throughout the set, with no mixing of conventions or stylistic deviations.
With 8 tools, the count is well-scoped for an image processing/generation server. Each tool serves a distinct and valuable function, covering core operations like generation, editing, and viewing without redundancy. The number is neither too sparse nor overwhelming for the domain.
The tool set provides comprehensive coverage for image generation and manipulation, including text-to-image, variations, conditioning, inpainting/outpainting, background removal, and viewing. A minor gap exists in lacking explicit editing tools like cropping or filtering, but core workflows are well-supported, and agents can likely work around this.