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gridhra/atx-mcp

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Deterministic, non-generative image transform MCP server for AI agents — straighten, crop, mask, layer, and encode with reproducible recipes and immutable originals.

MCP

gridhra/atx-mcp

Added 16 Sept 2026

#ai-agents #claude #deterministic #image-editing #image-processing #mcp #mcp-server #model-context-protocol

Overview

Deterministic, non-generative image transform MCP server for AI agents. It provides straighten, crop, mask, layer, and encode operations using reproducible recipes and immutable originals.

Best for

Best for
Developers building AI agents that need reliable, repeatable image transforms without generative noise.

Use cases

  • Straighten and crop images programmatically
  • Apply masking and layering to image assets
  • Encode images with reproducible transformations

How to use

Tools exposed

  • list_operations
  • explain_operation
  • import_asset
  • inspect_image
  • detect_tilt
  • detect_document
  • detect_text_blocks
  • generate_mask
  • render_preview
  • apply_transform
  • compare_revisions
  • list_assets
  • export_asset
  • linear_gradient
  • radial_gradient
  • luminosity_range
  • color_range

Notes

Deterministic, non-generative image transform MCP server for AI agents. It provides straighten, crop, mask, layer, and encode operations using reproducible recipes and immutable originals.

0 stars on GitHub. Last updated 2026-09-15. Licensed MIT.

Use cases

  • Straighten and crop images programmatically
  • Apply masking and layering to image assets
  • Encode images with reproducible transformations

Pros

  • Deterministic output with reproducible recipes
  • Immutable originals preserve source data
  • Rust implementation for performance

Cons

  • No public stars yet, indicating limited adoption
  • Non-generative, so cannot create or synthesize images
  • Niche use case may require custom integration

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Deterministic output with reproducible recipes
  • Immutable originals preserve source data
  • Rust implementation for performance

Cons

  • No public stars yet, indicating limited adoption
  • Non-generative, so cannot create or synthesize images
  • Niche use case may require custom integration

Pairs with

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