A
licenseA
qualityB
maintenanceExposes OpenAI's gpt-image-2 (image generation and editing) as an MCP server for tools like generate_image, edit_image, and iterative edit sessions.
6
21
11
MIT
Micu Image MCP
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TDQS
A4.3/5.0
Scored across 5 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: generate (text-to-image), edit (single image with optional mask), batch edit (N-to-N same operation), multi-reference (N-to-1 style fusion), and server_info (metadata). The WHEN TO USE sections explicitly disambiguate edge cases, making misselection unlikely.
Naming Consistency4/5
The image tools follow a consistent image_ prefix pattern (image_generate, image_edit, image_batch_edit, image_multi_reference). However, server_info breaks the verb_noun convention, and image_multi_reference uses a noun rather than a verb, creating slight inconsistency. Still, the pattern is predictable and readable.
Tool Count5/5
With 5 tools, the set is well-scoped for an image generation/editing server. Each tool covers a distinct workflow (single generation, single edit, batch edit, multi-reference fusion, and information), and none feel redundant or unnecessary. This is an ideal size for the domain.
Completeness5/5
The tool surface fully covers the core image workflows: generation from text, editing with masks and prompts, batch processing, multi-image reference fusion, and server configuration/limits. There are no apparent dead ends—any user request for image creation or modification can be routed to an appropriate tool. Missing operations like upscaling or dedicated background removal are achievable through existing tools (e.g., image_edit with mask or size parameters).
Maintenance
ActivityActive
ResponsivenessWithin a week