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Alternatives to image-gen-mcp

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    Related Servers

    • A
      license
      A
      quality
      B
      maintenance
      Generates and edits images using OpenAI GPT Image or Google Gemini models, saving every result to disk and returning local file paths so AI assistants can continue working with the images. It enables prompt-based image creation, editing, inpainting, multi-image composition, and model listing through MCP tools.
      1
      21 npm
      1
      Apache 2.0
    • A
      license
      Not graded
      quality
      Not graded
      maintenance
      Enables AI assistants to generate and edit images through OpenAI's DALL-E models via MCP tools. Supports text-to-image generation and image-to-image editing with configurable parameters for size, quality, and style.
      -
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI assistants and MCP clients to generate and edit images, automatically enriching prompts with visual details and quality presets across Gemini, OpenAI, and BytePlus Seedream, with results saved to disk.
      1,503 npm
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables any MCP-capable agent to generate images in the cloud without a GPU, local models, or downloads, using the user's own API key for providers such as DashScope's Wan/Qwen image models and Volcano Ark's Seedream. It can list which channels are configured and return generated image links or save the files to a local directory, all locally over stdio.
      MIT

    TDQS

    A3.7/5.0

    Scored across 2 tools

    Disambiguation5/5

    The two tools have completely distinct purposes: one lists available image models, the other generates images. No overlap or confusion possible.

    Naming Consistency5/5

    Both tools use a consistent verb_noun pattern in snake_case: 'list_image_models' and 'generate_image', making them predictable and easy to understand.

    Tool Count3/5

    With only 2 tools, the server feels under-scoped for its stated support of multiple providers and model selection. While minimal, it may be acceptable for a focused image generation task, but lacks coverage for related operations.

    Completeness3/5

    The server covers basic listing and generation, but lacks tools for retrieving generated images (URLs expire), managing providers, or handling model customization beyond the generate call. Minor gaps exist that could limit agent autonomy.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues