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read-jfif

Extract JFIF metadata from images to analyze embedded information like dimensions, compression, and thumbnail data for image processing workflows.

Instructions

Read JFIF metadata from an image

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYes

Implementation Reference

  • Core handler logic for the read-jfif tool: loads image buffer, configures exifr for JFIF segment, extracts and returns JFIF metadata.
    async (args, extra) => {
      try {
        const { image } = args;
        const buf = await loadImage(image);
        const opts = buildSegmentOptions(segment);
        const meta = await exifr.parse(buf, opts);
        
        const segmentKey = segment.toLowerCase();
        if (!meta || !meta[segmentKey]) {
          return createErrorResponse(`No ${segment} metadata found in image`);
        }
        
        return createSuccessResponse(meta);
      } catch (error) {
        return createErrorResponse(`Error reading ${segment} data: ${error instanceof Error ? error.message : String(error)}`);
      }
    }
  • Registration block defining and registering the read-jfif tool (along with other segment tools) to the MCP server.
    const segmentTools = [
      { name: 'read-icc', segment: 'ICC' },
      { name: 'read-iptc', segment: 'IPTC' },
      { name: 'read-jfif', segment: 'JFIF' },
      { name: 'read-ihdr', segment: 'IHDR' }
    ] as const;
    
    segmentTools.forEach(({ name, segment }) => {
      const segmentTool = server.tool(name,
        `Read ${segment} metadata from an image`,
        {
          image: ImageSourceSchema
        },
        async (args, extra) => {
          try {
            const { image } = args;
            const buf = await loadImage(image);
            const opts = buildSegmentOptions(segment);
            const meta = await exifr.parse(buf, opts);
            
            const segmentKey = segment.toLowerCase();
            if (!meta || !meta[segmentKey]) {
              return createErrorResponse(`No ${segment} metadata found in image`);
            }
            
            return createSuccessResponse(meta);
          } catch (error) {
            return createErrorResponse(`Error reading ${segment} data: ${error instanceof Error ? error.message : String(error)}`);
          }
        }
      );
      tools[name] = segmentTool;
    });
  • Zod input schema for image source parameter used by read-jfif tool.
      kind: z.enum(['path', 'url', 'base64', 'buffer']),
      path: z.string().optional(),
      url: z.string().optional(),
      data: z.string().optional(),
      buffer: z.string().optional()
    });
  • Helper function builds exifr parsing options specifically enabling only the JFIF segment.
    export function buildSegmentOptions(segment: 'ICC' | 'IPTC' | 'JFIF' | 'IHDR'): ExifrOptions {
      const options: ExifrOptions = {
        tiff: false,
        xmp: false,
        icc: false,
        iptc: false,
        jfif: false,
        ihdr: false,
      };
      
      const key = segment.toLowerCase() as 'icc' | 'iptc' | 'jfif' | 'ihdr';
      options[key] = true;
      
      return options;
    }
  • Helper function loads image from various sources (path, URL, base64, buffer) into a buffer for exifr parsing.
    export async function loadImage(src: ImageSourceType): Promise<Buffer | Uint8Array> {
      try {
        switch (src.kind) {
          case 'path':
            if (!src.path) {
              throw new Error('Path is required for kind="path"');
            }
            return await fs.promises.readFile(src.path);
          
          case 'url':
            if (!src.url) {
              throw new Error('URL is required for kind="url"');
            }
            
            if (src.url.startsWith('file://')) {
              // Handle file:// URLs by converting to filesystem path
              const filePath = fileURLToPath(src.url);
              return await fs.promises.readFile(filePath);
            } else {
              // Handle HTTP/HTTPS URLs
              const response = await fetch(src.url);
              if (!response.ok) {
                throw new Error(`Failed to fetch URL: ${response.status} ${response.statusText}`);
              }
              return new Uint8Array(await response.arrayBuffer());
            }
          
          case 'base64':
            if (!src.data) {
              throw new Error('Data is required for kind="base64"');
            }
            
            // Check for potential oversized base64 string (>30MB)
            if (src.data.length > 40000000) { // ~30MB in base64
              throw new Error('PayloadTooLarge: Base64 data exceeds 30MB limit');
            }
            
            // Handle data URIs or raw base64
            if (src.data.startsWith('data:')) {
              const base64Data = src.data.split(',')[1];
              return Buffer.from(base64Data, 'base64');
            } else {
              return Buffer.from(src.data, 'base64');
            }
          
          case 'buffer':
            if (!src.buffer) {
              throw new Error('Buffer is required for kind="buffer"');
            }
            return Buffer.from(src.buffer, 'base64');
          
          default:
            // This should never happen due to type constraints, but TypeScript needs it
            throw new Error(`Unsupported image source kind: ${(src as any).kind}`);
        }
      } catch (error) {
        if (error instanceof Error) {
          throw new Error(`Failed to load image: ${error.message}`);
        }
        throw error;
      }
    }
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 a read operation, implying it's non-destructive, but does not address potential errors (e.g., invalid image formats), performance aspects, or output format. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick comprehension.

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

Completeness2/5

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

Given the complexity of handling image metadata and the lack of annotations and output schema, the description is insufficient. It does not cover error handling, output details, or how JFIF metadata differs from other types, leaving the agent with incomplete context for effective tool 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 semantics beyond the input schema, which has 0% description coverage. It does not explain what 'image' represents or the meaning of its nested properties (e.g., 'kind', 'path', 'url'). However, with only one parameter, the baseline is higher, but the lack of any clarification keeps it at an adequate level.

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 clearly states the verb 'read' and the resource 'JFIF metadata from an image', making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'read-exif' or 'read-metadata', which might handle similar metadata types, so it falls short of a perfect score.

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, such as 'read-exif' or 'read-metadata' from the sibling list. It lacks context about JFIF-specific use cases or prerequisites, leaving the agent to infer usage based on tool names 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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