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duke0317

Image Processing MCP Server

by duke0317

adjust_sharpness

Adjust image sharpness by applying a factor to enhance or reduce edge definition for clearer or softer visual results.

Instructions

调整图片锐度

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
factorYes锐度调整因子,1.0为原始锐度,>1.0增强,<1.0减弱

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Core handler implementation: validates inputs, loads image, applies PIL ImageEnhance.Sharpness with given factor, outputs processed image as base64 JSON.
    async def adjust_sharpness(image_source: str, factor: float) -> list[TextContent]:
        """
        调整图片锐度
        
        Args:
            image_source: 图片数据(base64编码)或文件路径
            factor: 锐度调整因子(0.0-2.0)
            
        Returns:
            调整后的图片数据
        """
        try:
            # 验证参数
            if not image_source:
                raise ValidationError("图片数据不能为空")
            
            if not validate_numeric_range(factor, 0.0, 2.0):
                raise ValidationError(f"锐度因子必须在0.0-2.0范围内: {factor}")
            
            # 加载图片
            image = processor.load_image(image_source)
            
            # 调整锐度
            enhancer = ImageEnhance.Sharpness(image)
            enhanced_image = enhancer.enhance(factor)
            
            # 输出处理后的图片
            output_info = processor.output_image(enhanced_image, "sharpness")
            
            result = {
                "success": True,
                "message": f"锐度调整成功: 因子 {factor}",
                "data": {
                    **output_info,
                    "sharpness_factor": factor,
                    "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"锐度调整失败: {str(e)}"
            }
            return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))]
  • main.py:429-442 (registration)
    Registers the tool with FastMCP using @mcp.tool(). Defines input schema via Annotated fields and wraps the async handler call with safe_run_async for sync compatibility.
    @mcp.tool()
    def adjust_sharpness(
        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")],
        factor: Annotated[float, Field(description="锐度调整因子,1.0为原始锐度,>1.0增强,<1.0减弱", gt=0)]
    ) -> str:
        """调整图片锐度"""
        try:
            result = safe_run_async(color_adjust_sharpness(image_source, factor))
            return result[0].text
        except Exception as e:
            return json.dumps({
                "success": False,
                "error": f"调整锐度失败: {str(e)}"
            }, ensure_ascii=False, indent=2)
  • Explicit JSON schema definition for the adjust_sharpness tool input, used in get_color_adjust_tools() function (though registration uses Pydantic in main.py).
    Tool(
        name="adjust_sharpness",
        description="调整图片锐度",
        inputSchema={
            "type": "object",
            "properties": {
                "image_source": {
                    "type": "string",
                    "description": "图片数据(base64编码)或文件路径"
                },
                "factor": {
                    "type": "number",
                    "description": "锐度调整因子(0.0-2.0,1.0为原始锐度)",
                    "minimum": 0.0,
                    "maximum": 2.0
                }
            },
            "required": ["image_source", "factor"]
        }
    ),
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the action ('调整图片锐度') but doesn't describe what the tool actually does: whether it modifies the original image, creates a new image, requires specific image formats, has performance implications, or what the output looks like. For a mutation tool with zero annotation coverage, this is a significant gap.

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 ('调整图片锐度') that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple image processing operation and immediately communicates the core functionality.

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 that there's an output schema (though not shown), the description doesn't need to explain return values. However, for an image manipulation tool with no annotations, the description should provide more context about what the tool actually does to the image and when to use it. The current description is minimal but not completely inadequate given the schema coverage.

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%, so the schema already fully documents both parameters. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.

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 '调整图片锐度' (Adjust image sharpness) clearly states the verb (adjust) and resource (image sharpness). It distinguishes from siblings like 'adjust_brightness' or 'apply_sharpen' by focusing specifically on sharpness adjustment rather than brightness or a fixed sharpening operation. However, it doesn't explicitly differentiate from 'apply_sharpen' which might be a similar operation.

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 like 'apply_sharpen' or other adjustment tools. There's no mention of use cases, prerequisites, or comparisons with sibling tools. The agent must infer usage from the tool name alone.

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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