ModelScope Image Generation MCP Server
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- AlicenseCqualityCmaintenanceEnables users to generate high-quality images using ModelScope's Qwen-Image model through natural language prompts. Supports async task processing with both image URL and base64 encoded data output options.164 PyPI2MIT
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- AlicenseNot gradedqualityCmaintenanceEnables MCP clients to generate images from text prompts and edit existing PNG/JPEG files with natural-language instructions by driving a local ComfyUI instance running Qwen Image models. Supports aspect ratios, exact pixel dimensions, batch counts, and reproducible seeds, with matching capabilities exposed through a Python CLI.13,471 npm1MIT
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients on a local network to generate and edit images with a self-hosted uncensored Qwen-Image-2.1 model over Streamable HTTP. Supports text-to-image at any aspect ratio, multi-image editing, transparent backgrounds, seamless textures with height and normal maps, 360 panoramas, upscaling, and watermark removal.106MIT
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap in purpose. The tool 'generate_image' has a clear, singular function that cannot be confused with any other tool.
Since there is only one tool, it inherently follows a consistent naming pattern. The tool name 'generate_image' uses a verb_noun format, which is clear and appropriate for its function.
A single tool is too few for a server focused on image generation, as it lacks essential operations like listing models, managing generation parameters, or handling image variations. This minimal scope limits functionality and agent workflows.
The tool surface is severely incomplete for image generation; it only provides generation without supporting model selection, parameter tuning, or post-processing. This creates significant gaps that will hinder agent tasks requiring more control or information.