apply_blur
Apply a blur filter to images by specifying a radius value to control intensity, useful for privacy protection or artistic effects.
Instructions
应用模糊滤镜
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 | |
| radius | Yes | 模糊半径，值越大模糊效果越强 |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- tools/filters.py:179-232 (handler)Core handler function that applies BoxBlur filter to the image using PIL, handles validation, processes output with ImageProcessor, and returns JSON-formatted result.
async def apply_blur(image_source: str, radius: float) -> list[TextContent]: """ 应用模糊滤镜 Args: image_source: 图片源（文件路径或base64编码数据） radius: 模糊半径 Returns: 应用滤镜后的图片数据 """ try: # 验证参数 if not image_source: raise ValidationError("图片源不能为空") if not validate_numeric_range(radius, 0.1, 10.0): raise ValidationError(f"模糊半径必须在0.1-10.0范围内: {radius}") # 加载图片 image = processor.load_image(image_source) # 应用模糊滤镜 blurred_image = image.filter(ImageFilter.BoxBlur(radius)) # 输出处理后的图片 output_info = processor.output_image(blurred_image, "blur") result = { "success": True, "message": f"模糊滤镜应用成功: 半径 {radius}", "data": { **output_info, "filter_type": "blur", "radius": radius, "size": 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"模糊滤镜应用失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))] - main.py:240-254 (registration)MCP server tool registration with @mcp.tool() decorator. This wrapper calls the core handler from filters.py with safe_run_async and provides input validation via Annotated fields.
@mcp.tool() def apply_blur( 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")], radius: Annotated[float, Field(description="模糊半径，值越大模糊效果越强", ge=0.1)] ) -> str: """应用模糊滤镜""" try: result = safe_run_async(filters_apply_blur(image_source, radius)) return result[0].text except Exception as e: return json.dumps({ "success": False, "error": f"应用模糊效果失败: {str(e)}" }, ensure_ascii=False, indent=2) - tools/filters.py:28-42 (schema)JSON schema definition for the apply_blur tool input, defining properties for image_data (base64) and radius with range validation.
inputSchema={ "type": "object", "properties": { "image_data": { "type": "string", "description": "图片数据（base64编码）" }, "radius": { "type": "number", "description": "模糊半径（0.1-10.0）", "minimum": 0.1, "maximum": 10.0 } }, "required": ["image_data", "radius"]