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image-metadata-embed-and-report

image-metadata-embed-and-report

by Desmond-Labs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

create_metadata_report

Generate human-readable text reports from XMP metadata embedded in images. Choose detailed, simple, or JSON-only formats.

Instructions

Generate human-readable text reports from XMP metadata embedded in images

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image_path": "<image_path>",
      "format": "<format>"
    },
    "name": "create_metadata_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
formatYesReport format: detailed (full report), simple (key fields), json-only (raw data)
image_pathYesPath to image with embedded XMP metadata (e.g., "folder/image_me.jpg")
output_pathNoPath where report will be saved (auto-generated if not provided)
include_raw_jsonNoInclude raw JSON data in detailed format (default: true)
include_processing_infoNoInclude processing information in the report (default: true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It does not disclose that the tool writes a report file to disk (via output_path), whether the source image is ever modified, or what permissions are required — a significant gap for a tool with a filesystem side effect.

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

Conciseness4/5

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

A single front-loaded sentence with no filler or redundancy. It is efficient, though its brevity is part of why behavioral and usage context is missing.

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?

With 5 parameters, no output schema, and no annotations, the description should explain the write-to-file behavior and output handling. Instead it covers only the headline purpose, leaving the agent without the context needed to call it safely.

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 coverage is 100%, and each parameter including the format enum is documented in the schema itself, so the baseline is 3. The description adds no parameter detail beyond the schema, and actually sits in mild tension with the json-only format, which is not 'human-readable text'.

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?

States a specific verb (Generate) and resource (human-readable text reports from XMP metadata in images), which clearly separates it from read_image_metadata and embed_image_metadata. It does not explicitly name the sibling it replaces, so it falls just short of a 5.

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 offers no when-to-use guidance, no prerequisites, and never mentions alternatives such as read_image_metadata for simply inspecting values. An agent must infer the use case entirely from the name and purpose.

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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