muse-image-mcp
Related Servers
Alternatives to muse-image-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityAmaintenanceEnables 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.1,984 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to generate and edit images through Gemini, OpenAI, or BytePlus Seedream, automatically expanding brief requests into detailed prompts with quality presets. Generated files are saved to disk and returned to the client as MCP resources.1,984 npmMIT
- AlicenseNot gradedqualityCmaintenanceEnables MCP clients to generate and edit images through any OpenAI-compatible image API, saving results as local files and returning absolute paths for immediate use.18 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables generating and editing images from MCP clients by automatically optimizing prompts, applying quality presets, and routing requests to Gemini, OpenAI, or BytePlus Seedream, with results saved to disk and returned as resources.1,984 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables generating and editing images from MCP clients with automatic prompt optimization and quality presets, supporting Gemini, OpenAI GPT Image, and BytePlus Seedream.1,984 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to generate and edit images via Gemini, OpenAI, or BytePlus Seedream, with automatic prompt optimization and quality presets, saving outputs to disk and returning them as MCP resources.1,984 npmMIT
TDQS
Scored across 3 tools
generate_image (text-to-image) is clearly distinct, but edit_image (single-turn edit from existing images) and iterate_image (multi-turn conversational edit) overlap in purpose. The descriptions do clarify the difference via the response_id/previous_response_id mechanic, so an agent can likely distinguish them.
All three tools follow a consistent verb_noun pattern: generate_image, edit_image, iterate_image. The convention is predictable and uniform.
Three tools is on the lean side but appropriate for a focused image generation/editing service. Each tool covers a distinct mode (create, single edit, iterative edit), though the surface feels slightly thin.
The core image workflow (generate, edit, iterate) is covered, and tool outputs return file paths with clear semantics. Missing extras like batch generation, upscaling, or listing past generations are minor gaps an agent can work around.