A
licenseNot graded
qualityC
maintenanceEnables MCP-capable agents to generate and edit images through Gemini or OpenAI, returning an absolute file path instead of image bytes to keep context windows clean.
MIT
muse-image-mcp
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TDQS
A3.9/5.0
Scored across 3 tools
Disambiguation4/5
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.
Naming Consistency5/5
All three tools follow a consistent verb_noun pattern: generate_image, edit_image, iterate_image. The convention is predictable and uniform.
Tool Count4/5
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.
Completeness4/5
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.