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photo_image_diff

Image Diff — Compare two images and highlight the differences visually. Takes two separately-named uploads: 'image1' and 'image2'. [category: photo]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fuzzNoPer-pixel tolerance percent, 0-20.
image1YesFirst image — JPG, PNG, WebP, BMP (max 25MB)
image2YesSecond image — JPG, PNG, WebP, BMP (max 25MB)
normalizeNoNormalize sizes before comparing.
output_formatNoFormat of the returned diff image. AE/SSIM/PSNR/RMSE scores ride X-JE-Metric-* response headers, not the body.png
lowlight_colorNoHex color for unchanged pixels.#222222
highlight_colorNoHex color for changed pixels.#ff0000

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already cover the safety profile (readOnlyHint=false, destructiveHint=false), and the description adds the visual-diff behavior. However, it does not disclose what happens with mismatched sizes, whether uploads are consumed, or what the response contains beyond a direct reading of the parameter descriptions.

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?

The description is compact and front-loaded with the core function, followed by a sentence clarifying the required upload names. The only minor redundancy is the 'Image Diff —' prefix mirroring the annotation title, but overall there is no wasted content.

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?

The description plus the rich 100%-covered schema give enough to invoke the tool correctly, and the output_format parameter description explains the returned image and metric headers. Still, the description itself omits practical context such as how normalize affects comparison and what happens if images differ in size.

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 documents every parameter. The description only reiterates the names 'image1' and 'image2' without adding new semantic depth, matching the baseline for fully self-documenting schemas.

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 a specific verb and resource: 'Compare two images and highlight the differences visually.' This is sufficient to distinguish it from similarity-analysis or overlay tools in the sibling list, though it does not explicitly name an alternative.

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?

No guidance is given about when to choose this tool over alternatives such as analyze_image_similarity or photo_image_overlay. There are no scenarios, prerequisites, or exclusions stated, leaving the agent to infer appropriate usage.

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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TDQS

B3.2/5.0
Disambiguation2/5

Multiple tool pairs are near-identical: octopus_mkdir/octopus_make_folder and octopus_move/octopus_move_file are literal duplicates, analyze_hash/generate_hash both compute hashes, convert_word_to_pdf overlaps convert_document, and photo_compress/photo_compress_to_size plus pdf_thumbnails/pdf_to_images have fuzzy boundaries. The descriptions are detailed and cross-reference each other helpfully, but at 144 tools an agent will regularly misselect.

Naming Consistency3/5

The dominant {category}_{verb}_{object} snake_case pattern (pdf_*, photo_*, convert_*, analyze_*, media_*) is largely consistent and predictable. However, outliers like chatwithyourpdf and describe_image break the category-prefix convention, and the octopus namespace mixes bare verbs (read, write, mkdir) with verb_noun forms (make_folder, move_file, search_meta) inconsistently.

Tool Count2/5

144 tools is an extreme count for any MCP server. The broad scope (PDF, photo, video, audio, conversion, analysis, generation, file storage, web, e-sign) justifies some volume, but the count is inflated by batch and inspect variants (pdf_to_excel + batch + inspect), duplicate tools, and overlapping converters. An agent faces an overwhelming selection surface.

Completeness4/5

Per-domain coverage is remarkably deep: PDF spans merge/split/compress/protect/unlock/metadata/OCR/watermark and bidirectional conversion; photo covers editing, format conversion, face handling, OCR, and collage; file storage has full CRUD plus search. Minor gaps exist (no audio transcription, no video metadata editing, no deletion of PDF pages is actually covered via pdf_delete_pages) but the surface has no dead ends for its declared domains.

Resources