Skip to main content
Glama
duke0317

Image Processing MCP Server

by duke0317

apply_sharpen

Enhance image clarity and detail by applying a sharpening filter to improve edge definition and visual crispness.

Instructions

应用锐化滤镜

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_sourceYes图片源,可以是文件路径或base64编码的图片数据

Implementation Reference

  • The core asynchronous handler function that loads the image, applies the SHARPEN filter using PIL, processes the output, and returns JSON-formatted results.
    async def apply_sharpen(image_data: str) -> list[TextContent]:
        """
        应用锐化滤镜
        
        Args:
            image_data: 图片数据(base64编码)
            
        Returns:
            应用滤镜后的图片数据
        """
        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)
            
            # 应用锐化滤镜
            sharpened_image = image.filter(ImageFilter.SHARPEN)
            
            # 输出处理后的图片
            output_info = processor.output_image(sharpened_image, "sharpen")
            
            result = {
                "success": True,
                "message": "锐化滤镜应用成功",
                "data": {
                    **output_info,
                    "filter_type": "sharpen",
                    "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"锐化滤镜应用失败: {str(e)}"
            }
            return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))]
  • Tool schema definition including input schema for the apply_sharpen tool, part of get_filter_tools() function.
    Tool(
        name="apply_sharpen",
        description="应用锐化滤镜",
        inputSchema={
            "type": "object",
            "properties": {
                "image_data": {
                    "type": "string",
                    "description": "图片数据(base64编码)"
                }
            },
            "required": ["image_data"]
        }
  • main.py:270-282 (registration)
    MCP server tool registration using @mcp.tool() decorator, which wraps and calls the handler from filters.py via safe_run_async.
    @mcp.tool()
    def apply_sharpen(
        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")]
    ) -> str:
        """应用锐化滤镜"""
        try:
            result = safe_run_async(filters_apply_sharpen(image_source))
            return result[0].text
        except Exception as e:
            return json.dumps({
                "success": False,
                "error": f"应用锐化效果失败: {str(e)}"
            }, ensure_ascii=False, indent=2)

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/duke0317/ps-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server