wikivibe-images-mcp
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- AlicenseNot gradedqualityBmaintenanceEnables coding agents and other MCP clients to generate images from text prompts and edit, restyle, or extend existing images through an OpenAI-compatible /v1/images API. Results are returned both inline as image content blocks and as files saved to disk, so agents can view and reuse them within a normal task.25 npm1MIT
- AlicenseNot gradedqualityBmaintenanceEnables any MCP-compatible agent to generate and edit images using an existing Codex/ChatGPT subscription, exposing image generation and editing as tool calls.284 npmMIT
- AlicenseNot gradedqualityCmaintenanceEnables 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.1MIT
- AlicenseAqualityBmaintenanceGenerates and edits images using OpenAI GPT Image or Google Gemini models, saving every result to disk and returning local file paths so AI assistants can continue working with the images. It enables prompt-based image creation, editing, inpainting, multi-image composition, and model listing through MCP tools.114 npm1Apache 2.0
- AlicenseNot gradedqualityAmaintenanceEnables 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.1,984 npmMIT
- AlicenseAqualityAmaintenanceEnables coding agents to generate and edit images using Gemini and OpenAI image models, saving files directly into the project with configurable providers, models, and security restrictions.301MIT
TDQS
Scored across 3 tools
The three tools have clearly distinct purposes: generate_image creates images, edit_image modifies existing images, and list_image_models enumerates available models. There is no overlap in resource or action, so an agent can easily select the right tool.
All names use consistent snake_case with an imperative verb first: generate_image, edit_image, list_image_models. The pattern is predictable across the set, with only a minor difference in noun length for the listing tool.
Three tools is a focused, reasonable size for an image-generation server, and each tool earns its place. It is slightly thin compared to servers that include retrieval or history operations, but not problematically so.
The set covers the core lifecycle: generating images, editing them, and discovering available models. Minor gaps exist, such as no explicit retrieval or deletion of generated images, but the primary workflows are supported.