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imagengen

by danton721
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      Enables generating and editing images from any MCP client by rewriting requests into detailed prompts and routing them to Gemini, OpenAI, or BytePlus Seedream, with configurable quality presets, aspect ratios, and image sizes. Results are saved to disk and returned to the assistant as MCP resources.
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    • imaginate-mcp

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    • mcp-image

      magdalenanam615
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      Enables MCP clients such as Codex, Cursor, and Claude Code to generate and edit images by optimizing prompts before sending them to Gemini, OpenAI, or BytePlus Seedream, with quality presets and configurable output. Generated files are saved to disk and returned to the assistant as MCP resources.
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    • MCP Image Generator

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      Enables assistants in any MCP client to generate new images or edit existing ones, automatically enriching short requests with composition, lighting, and camera details before sending them to Gemini, OpenAI, or BytePlus Seedream. Supports quality presets, aspect ratios, image sizes, character consistency, blended inputs, and Google Search grounding for current information.
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.9/5.0

    Scored across 3 tools

    Disambiguation5/5

    The three tools are clearly distinct: one lists available providers, one does text-to-image generation, and one does image-to-image editing/transformation. The descriptions make it obvious which tool to use for each task, and the coverage of providers in each is well documented.

    Naming Consistency3/5

    The naming pattern is inconsistent: 'list_image_providers' uses snake_case with a verb_noun pattern, while 'text-to-image' and 'image-to-image' use a hyphenated adjective-noun naming convention that describes the task rather than an action. The latter two don't follow a verb-leading pattern.

    Tool Count4/5

    Three tools is a compact, well-scoped set that covers the core capabilities of an image generation server: discovery/configuration, generation, and editing. It's on the lean side but appropriate for the narrow domain; each tool serves a distinct and necessary purpose.

    Completeness3/5

    The server covers the primary workflows (discover providers, generate, edit). However, there are notable gaps: no tool to view/retrieve generated images or their metadata, no batch generation, no image style/variation features, and no way to delete or manage saved images. The core generate/edit flows work but retrieval and management are missing.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources