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Related Servers

Alternatives to prompt-to-asset

No user-submitted related servers found.

    Related Servers

    • A
      license
      C
      quality
      A
      maintenance
      Generate deployment-ready PWA, iOS, and Android app icon sets from a text description. Produces all 27 required sizes, maskable icons, Xcode-ready iOS icons, Android mipmap folders, splash screens, and manifest.json packaged as a ZIP. Powered by Google Imagen 4.
      6
      2
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables generating images, animating stills or generating video from prompts through OpenRouter, Mistral, Eden AI and fal.ai via direct REST calls, with async job polling, reference images, palettes, exclusions and per-model options. Also performs local background removal, animated GIF assembly, cropping, format conversion, resizing, and single- or multi-size Windows ICO icon set building and unpacking.
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      Visual icon search, retrieval, and comparison for AI agents. Search 200k+ icons semantically, render side-by-side comparison grids, and retrieve raw SVG markup — all tools return images so vision-capable LLMs can see the icons.
      1
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    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI assistants to search 2000+ bundled Lucide icons by keyword and generate one or many styled SVGs with consistent stroke width, color, and size, entirely offline without API keys.
      MIT

    TDQS

    A3.7/5.0

    Scored across 24 tools

    Disambiguation4/5

    The asset_generate_* family is clearly separated by output artifact, and the processing, ingestion, validation, and export tools have mostly distinct jobs. The main ambiguity is between asset_doctor and asset_capabilities, which both report currently available execution modes, and asset_export_bundle vs asset_save_inline_svg both create platform bundles from different input types.

    Naming Consistency3/5

    All tools share the asset_ prefix and snake_case, so the surface is readable, but the internal convention is inconsistent: verb-first names like asset_generate_logo and asset_init_brand coexist with object-first names like asset_models_list, asset_models_inspect, and asset_brand_bundle_parse, plus noun-only names like asset_sprite_sheet and asset_capabilities. The asset_generate_* subfamily is consistent, but the rest does not follow one clearly predictable pattern.

    Tool Count3/5

    At 24 tools, the set is at the heavy/borderline end for an MCP server: each tool has a real purpose, but several generate_* variants and model/doctor helpers could be consolidated or parameterized to make the surface easier for an agent to discover. The count is not egregious, but it is above the ideal compact size.

    Completeness5/5

    The tool surface covers the full lifecycle from brand ingestion and prompt enhancement through generation, saving/ingestion, validation, vectorization, upscaling, background removal, platform export, sprite sheets, 9-slice config, model inspection, and environment diagnosis. Every execution mode has a clear follow-up path, so the workflow does not end in a dead end.

    Maintenance

    ActivityStale
    ResponsivenessUnresponsive