A
licenseA
qualityB
maintenanceEnables image generation and editing through OpenAI's gpt-image-2 model, supporting text-to-image, reference-guided generation, and editing with local files or URLs.
2
388
2
MIT
ai-media-mcp
Related Servers
Alternatives to ai-media-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityCmaintenanceEnables intelligent multi-provider image generation through OpenAI and Google Gemini APIs with automatic provider selection, support for reference images, real-time data grounding, and conversational refinement.8MIT
- FlicenseNot gradedqualityDmaintenanceEnables image generation, editing, and refinement using Google's Gemini 2.5 Flash Image model with support for multi-image composition and style transfer.-
- FlicenseNot gradedqualityDmaintenanceEnables AI-powered image generation using Google's Gemini 2.5 Flash Image Preview model, supporting text-to-image and image-to-image generation through the MCP interface.-
- AlicenseAqualityBmaintenanceEnables AI assistants to generate real images via multiple models (OpenAI, Gemini, Recraft, Seedream, Grok, Arrow) and returns usable file paths instead of base64 data.1186MIT
- AlicenseAqualityBmaintenanceEnables generating and editing images via OpenAI GPT Image 2.5 models (flare and sunburst) through MCP tools for text-to-image and reference-based image editing.2MIT
TDQS
A4.2/5.0
Scored across 6 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: local capability lookup vs. live probing, image generation vs. video generation, async submission vs. status polling vs. synchronous wait-and-download. Even related pairs like generate_video and generate_video_and_wait are cleanly separated by behavior.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern: list_, probe_, generate_, get_. The longer generate_video_and_wait is still a predictable extension of generate_video without mixing conventions.
Tool Count5/5
With six tools, the server is well-scoped for an AI media generation service. Each tool addresses a distinct part of the workflow: capability discovery, image generation, video submission, status polling, and synchronous video generation.
Completeness4/5
The surface covers the core workflow: check capabilities, generate an image, submit/synchronously wait for video, and poll video status. Minor gaps exist, such as no way to cancel a pending video job or list/manage previously generated files, but these are not critical to the server's stated purpose.
Maintenance
ActivityMaintained
ResponsivenessNo issues