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duke0317

Image Processing MCP Server

by duke0317

adjust_brightness

Adjust image brightness by modifying a factor value to lighten or darken images for better visibility and visual quality.

Instructions

调整图片亮度

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亮度调整因子,1.0为原始亮度,>1.0变亮,<1.0变暗

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Core handler function that loads the image, applies brightness adjustment using PIL ImageEnhance.Brightness, processes output, and returns JSON result.
    async def adjust_brightness(image_source: str, factor: float) -> list[TextContent]:
        """
        调整图片亮度
        
        Args:
            image_source: 图片数据(base64编码)或文件路径
            factor: 亮度调整因子(0.0-2.0)
            
        Returns:
            调整后的图片数据
        """
        try:
            # 验证参数
            if not image_source:
                raise ValidationError("图片数据或路径不能为空")
            
            if not validate_numeric_range(factor, 0.0, 2.0):
                raise ValidationError(f"亮度因子必须在0.0-2.0范围内: {factor}")
            
            # 加载图片
            image = processor.load_image(image_source)
            
            # 调整亮度
            enhancer = ImageEnhance.Brightness(image)
            enhanced_image = enhancer.enhance(factor)
            
            # 输出处理后的图片
            output_info = processor.output_image(enhanced_image, "brightness")
            
            result = {
                "success": True,
                "message": f"亮度调整成功: 因子 {factor}",
                "data": {
                    **output_info,
                    "brightness_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))]
  • main.py:384-398 (registration)
    Registers the 'adjust_brightness' tool with FastMCP server using @mcp.tool decorator, defines input parameters with descriptions, and delegates execution to the imported handler via safe_run_async.
    @mcp.tool()
    def adjust_brightness(
        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="亮度调整因子,1.0为原始亮度,>1.0变亮,<1.0变暗", gt=0)]
    ) -> str:
        """调整图片亮度"""
        try:
            result = safe_run_async(color_adjust_brightness(image_source, factor))
            return result[0].text
        except Exception as e:
            return json.dumps({
                "success": False,
                "error": f"调整亮度失败: {str(e)}"
            }, ensure_ascii=False, indent=2)
  • Explicit JSON schema definition for the adjust_brightness tool input validation, part of get_color_adjust_tools() function.
    Tool(
        name="adjust_brightness",
        description="调整图片亮度",
        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": "亮度调整因子(0.0-2.0,1.0为原始亮度)",
                    "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?

No annotations are provided, so the description carries full burden. '调整图片亮度' implies a transformation operation but doesn't disclose whether this modifies the original image or creates a copy, what permissions are needed, performance characteristics, or what the output contains. For a mutation tool with zero annotation coverage, this is insufficient behavioral disclosure.

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. It's front-loaded with the core purpose and contains zero wasted words. For a simple image adjustment operation, this brevity is appropriate and efficient.

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 an output schema exists (which presumably describes the return value), the description doesn't need to explain return values. However, for an image transformation tool with no annotations, the description should provide more context about the operation's behavior, side effects, and usage patterns. The current description is minimal but functional given the structured data available.

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 both parameters well-documented in the schema. The description adds no parameter information beyond what's already in the schema. According to guidelines, 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 '调整图片亮度' (adjust image brightness) clearly states the verb (adjust) and resource (image brightness). It's specific about the operation but doesn't differentiate from sibling tools like 'adjust_contrast' or 'adjust_gamma' - all are image adjustment operations. The purpose is clear but lacks sibling distinction.

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. With many sibling tools for image adjustment (contrast, gamma, saturation, etc.), there's no indication of when brightness adjustment is appropriate versus other adjustments. No context about prerequisites or exclusions is provided.

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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