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duke0317

Image Processing MCP Server

by duke0317

apply_emboss

Adds a 3D embossed effect to images by applying a filter that creates raised or engraved appearance for visual enhancement.

Instructions

应用浮雕滤镜

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Core handler function that loads the image, applies PIL's ImageFilter.EMBOSS filter, processes output via ImageProcessor, and returns JSON result as TextContent.
    async def apply_emboss(image_data: str) -> list[TextContent]:
        """
        应用浮雕滤镜
        
        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)
            
            # 应用浮雕滤镜
            embossed_image = image.filter(ImageFilter.EMBOSS)
            
            # 输出处理后的图片
            output_info = processor.output_image(embossed_image, "emboss")
            
            result = {
                "success": True,
                "message": "浮雕滤镜应用成功",
                "data": {
                    **output_info,
                    "filter_type": "emboss",
                    "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"浮雕滤镜应用失败: {str(e)}"
            }
            return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))]
  • main.py:299-310 (registration)
    MCP tool registration via @mcp.tool() decorator. This wrapper function delegates to the filters.apply_emboss handler using safe_run_async.
    def apply_emboss(
        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:
        """应用浮雕滤镜"""
        try:
            result = safe_run_async(filters_apply_emboss(image_source))
            return result[0].text
        except Exception as e:
            return json.dumps({
                "success": False,
                "error": f"应用浮雕效果失败: {str(e)}"
            }, ensure_ascii=False, indent=2)
  • Tool schema definition in get_filter_tools(), specifying input schema for 'image_data' parameter, though primarily used via main.py decorator.
    Tool(
        name="apply_emboss",
        description="应用浮雕滤镜",
        inputSchema={
            "type": "object",
            "properties": {
                "image_data": {
                    "type": "string",
                    "description": "图片数据(base64编码)"
                }
            },
            "required": ["image_data"]
        }
    ),
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('apply') but doesn't describe what the tool actually does behaviorally: whether it modifies the image in-place, returns a new image, requires specific image formats, has performance implications, or what the output looks like. For a tool with no annotations, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—just four characters in Chinese ('应用浮雕滤镜'). It's front-loaded with the core action and resource, with zero wasted words. This is appropriately sized for a simple, single-purpose tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (image processing filter), no annotations, and the presence of an output schema (which likely describes the result), the description is minimally adequate. It states what the tool does but lacks context about behavior, usage, or effects. The output schema may cover return values, but the description doesn't provide enough standalone guidance for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds no parameter-specific information beyond what's in the schema. However, with 100% schema description coverage (the single parameter 'image_source' is well-documented in the schema), the baseline score is 3. The description doesn't compensate but doesn't need to since the schema fully covers the parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description '应用浮雕滤镜' (apply emboss filter) clearly states the verb ('apply') and resource ('emboss filter'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'apply_blur' or 'apply_sepia' by specifying the exact filter type. However, it doesn't explicitly mention the target resource (image), which is implied but could be more explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't explain what an emboss filter does, when it's appropriate (e.g., for artistic effects, texture enhancement), or how it differs from similar tools like 'apply_contour' or 'apply_edge_enhance' in the sibling list. There's no mention of prerequisites or context for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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