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duke0317

Image Processing MCP Server

by duke0317

apply_invert

Invert colors in images to create negative effects or correct color issues. Process images by providing file paths or base64 data for color reversal.

Instructions

应用反色滤镜

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 color inversion using PIL's ImageOps.invert, processes output via ImageProcessor, and returns JSON result.
    async def apply_invert(image_data: str) -> list[TextContent]:
        """
        应用反色滤镜
        
        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)
            
            # 应用反色滤镜
            inverted_image = ImageOps.invert(image.convert('RGB'))
            
            # 输出处理后的图片
            output_info = processor.output_image(inverted_image, "invert")
            
            result = {
                "success": True,
                "message": "反色滤镜应用成功",
                "data": {
                    **output_info,
                    "filter_type": "invert",
                    "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"反色滤镜应用失败: {str(e)}"
            }
            return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))]
  • main.py:368-381 (registration)
    Registers the apply_invert tool with the MCP server using the @mcp.tool decorator. Defines the input schema via Annotated Field and wraps the call to the core filters.apply_invert handler.
    @mcp.tool()
    def apply_invert(
        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:
        """应用反色滤镜"""
        try:
            result = safe_run_async(filters_apply_invert(image_source))
            return result[0].text
        except Exception as e:
            return json.dumps({
                "success": False,
                "error": f"应用反色效果失败: {str(e)}"
            }, ensure_ascii=False, indent=2)
  • Tool schema definition for apply_invert, including input schema for image_data parameter, as part of get_filter_tools().
    Tool(
        name="apply_invert",
        description="应用反色滤镜",
        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 full burden but offers minimal behavioral information. It states the action ('apply invert filter') but doesn't disclose whether this is a destructive operation, what permissions are needed, rate limits, or output format. The agent must infer behavior from context, leaving significant gaps.

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 a single, efficient phrase ('应用反色滤镜') that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the core action, earning full marks for conciseness.

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 low complexity (one parameter, simple operation), the presence of an output schema (which handles return values), and 100% schema coverage, the description is minimally adequate. However, it lacks context on behavioral aspects like side effects or error conditions, making it incomplete for safe use without annotations.

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?

Schema description coverage is 100%, with the parameter 'image_source' well-documented as accepting file paths or base64 data. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline of 3. No additional semantics are offered.

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 '应用反色滤镜' (apply invert filter) clearly states the verb 'apply' and the resource 'invert filter', making the purpose immediately understandable. It distinguishes from siblings like 'apply_blur' or 'apply_sepia' by specifying the specific filter type. However, it doesn't explicitly mention it operates on images, though this is implied by context.

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 mention what 'invert' does (negating colors), when it's appropriate (e.g., for artistic effects or enhancing contrast), or suggest sibling tools like 'convert_to_grayscale' for different transformations. Usage is implied only by the tool name in context.

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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