crop_image
Crop images by specifying pixel coordinates to remove unwanted areas or focus on specific sections. Define left, top, right, and bottom boundaries to extract desired portions from image files or base64 data.
Instructions
裁剪图片
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| image_source | Yes | 图片源，可以是文件路径或base64编码的图片数据 | |
| left | Yes | 裁剪区域左边界坐标（像素） | |
| top | Yes | 裁剪区域上边界坐标（像素） | |
| right | Yes | 裁剪区域右边界坐标（像素） | |
| bottom | Yes | 裁剪区域下边界坐标（像素） |
Implementation Reference
- tools/transform.py:229-284 (handler)Core handler function that loads the image, validates crop coordinates, performs the crop using PIL Image.crop, and returns JSON result with base64 output.async def crop_image(image_data: str, left: int, top: int, right: int, bottom: int) -> list[TextContent]: """ 裁剪图片 Args: image_data: 图片数据（base64编码） left, top, right, bottom: 裁剪坐标 Returns: 裁剪后的图片数据 """ try: # 验证参数 if not image_data: raise ValidationError("图片数据不能为空") # 加载图片 image = processor.load_image(image_data) image_width, image_height = image.size # 验证裁剪坐标 if not validate_crop_coordinates(left, top, right, bottom, image_width, image_height): raise ValidationError(f"无效的裁剪坐标: ({left}, {top}, {right}, {bottom}), 图片尺寸: {image_width}x{image_height}") # 裁剪图片 cropped_image = image.crop((left, top, right, bottom)) # 输出裁剪后的图片 output_info = processor.output_image(cropped_image, f"crop_{left}_{top}_{right}_{bottom}") result = { "success": True, "message": f"图片裁剪成功: ({left}, {top}, {right}, {bottom})", "data": { **output_info, "original_size": (image_width, image_height), "crop_box": (left, top, right, bottom), "cropped_size": cropped_image.size } } return [TextContent(type="text", text=json.dumps(result, ensure_ascii=False))] except ValidationError as e: error_result = { "success": False, "error": f"参数验证失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))] except Exception as e: error_result = { "success": False, "error": f"图片裁剪失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))]
- main.py:188-204 (registration)Registers the crop_image tool with the FastMCP server (@mcp.tool()), providing input schema via Annotated Fields, and delegates execution to the transform_crop_image handler.@mcp.tool() def crop_image( image_source: Annotated[str, Field(description="图片源，可以是文件路径或base64编码的图片数据")], left: Annotated[int, Field(description="裁剪区域左边界坐标（像素）", ge=0)], top: Annotated[int, Field(description="裁剪区域上边界坐标（像素）", ge=0)], right: Annotated[int, Field(description="裁剪区域右边界坐标（像素）", gt=0)], bottom: Annotated[int, Field(description="裁剪区域下边界坐标（像素）", gt=0)] ) -> str: """裁剪图片""" try: result = safe_run_async(transform_crop_image(image_source, left, top, right, bottom)) return result[0].text except Exception as e: return json.dumps({ "success": False, "error": f"裁剪图片失败: {str(e)}" }, ensure_ascii=False, indent=2)
- tools/transform.py:48-79 (schema)Defines the input schema for the crop_image tool as part of get_transform_tools(), including properties and validation rules (though registration uses pydantic in main.py).Tool( name="crop_image", description="裁剪图片", inputSchema={ "type": "object", "properties": { "image_data": { "type": "string", "description": "图片数据（base64编码）" }, "left": { "type": "integer", "description": "左边界坐标", "minimum": 0 }, "top": { "type": "integer", "description": "上边界坐标", "minimum": 0 }, "right": { "type": "integer", "description": "右边界坐标" }, "bottom": { "type": "integer", "description": "下边界坐标" } }, "required": ["image_data", "left", "top", "right", "bottom"] } ),