AWS Nova Canvas MCP Server

Instructions

Generate an image that follows the layout and composition of a reference image.

Args:
    image_path: File path of the reference image
    prompt: Text describing the image to be generated
    negative_prompt: Text specifying attributes to exclude from generation
    control_mode: Control mode (CANNY_EDGE, etc.)
    height: Output image height (pixels)
    width: Output image width (pixels)
    cfg_scale: Prompt matching degree (1-20)
    
Returns:
    Dict: Dictionary containing the file path of the generated image

Implementation Reference

• aws_nova_canvas_mcp/tools/image_conditioning.py:12-75 (handler)

  The primary handler function implementing the image_conditioning tool. It processes a reference image, generates a conditioned image using Bedrock API, saves it, and returns the path.

  async def image_conditioning(
          image_path: str,
          prompt: str,
          negative_prompt: str = "",
          control_mode: str = "CANNY_EDGE",
          height: int = 512,
          width: int = 512,
          cfg_scale: float = 8.0,
          output_path: str = None,
  ) -> Dict[str, Any]:
      """
      Generate an image that follows the layout and composition of a reference image.
      
      Args:
          image_path: File path of the reference image
          prompt: Text describing the image to be generated
          negative_prompt: Text specifying attributes to exclude from generation
          control_mode: Control mode (CANNY_EDGE, etc.)
          height: Output image height (pixels)
          width: Output image width (pixels)
          cfg_scale: Prompt matching degree (1-20)
          output_path: Absolute path to save the image
          
      Returns:
          Dict: Dictionary containing the file path of the generated image
      """
      try:
          # Read image file and encode to base64
          with open(image_path, "rb") as image_file:
              input_image = base64.b64encode(image_file.read()).decode('utf8')
  
          body = json.dumps({
              "taskType": "TEXT_IMAGE",
              "textToImageParams": {
                  "text": prompt,
                  "negativeText": negative_prompt,
                  "conditionImage": input_image,
                  "controlMode": control_mode
              },
              "imageGenerationConfig": {
                  "numberOfImages": 1,
                  "height": height,
                  "width": width,
                  "cfgScale": cfg_scale
              }
          })
  
          # Generate image
          image_bytes = generate_image(body)
  
          # Save image
          image_info = save_image(image_bytes, output_path=output_path)
  
          # Generate result
          result = {
              "image_path": image_info["image_path"],
              "message": f"Image conditioning completed successfully. Saved location: {image_info['image_path']}"
          }
  
          return result
  
      except Exception as e:
          raise McpError(f"Error occurred while image conditioning: {str(e)}")

• aws_nova_canvas_mcp/server.py:35-35 (registration)

  Registration point for the image_conditioning tool in the MCP server (currently commented out).

  # mcp.add_tool(image_conditioning)

• aws_nova_canvas_mcp/server.py:9-9 (registration)

  Import statement bringing the image_conditioning handler into the server module.

  from .tools.image_conditioning import image_conditioning