Project description
imagine-mcp
mcp-name: io.github.n24q02m/imagine-mcp
Image and video understanding + generation for AI agents — across Gemini, OpenAI, and Grok.
Sister projects from n24q02m (click to expand)
| Project | Tagline | Tag |
|---|---|---|
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| claude-plugins | Claude Code plugin marketplace for the n24q02m MCP servers — install web sea… | Marketplace |
| imagine-mcp | Image and video understanding + generation for AI agents — across Gemini, Op… | MCP |
| jules-task-archiver | Chrome Extension for bulk operations on Jules tasks via batchexecute API — a… | Tooling |
| mcp-core | Shared foundation for building MCP servers — Streamable HTTP transport, OAut… | MCP |
| mnemo-mcp | Persistent AI memory with hybrid search and embedded sync. Open, free, unlimi… | MCP |
| qwen3-embed | Lightweight Qwen3 text embedding and reranking via ONNX Runtime and GGUF | Library |
| skret | Secrets without the server. | CLI |
| tacet | TACET: a self-distilling neuro-symbolic cascade that amortises LLM cost in kn… | Tooling |
| web-core | Shared web infrastructure package for search, scraping, HTTP security, and st… | Library |
| wet-mcp | Open-source MCP server for AI agents: web search, content extraction, and lib… | MCP |
Table of contents
- Features
- Install
- Smithery
- Configuration
- CLI
- Hosted endpoint
- Documentation
- Tools
- Comparison
- Security
- Build from Source
- Deploy to Cloudflare
- Trust Model
- Contributing
- License
Features
- Multimodal understanding — Describe, classify, or reason over images and videos (Gemini handles mixed image + video in one call)
- Image generation — Text-to-image and image-to-image (edit / inpaint) across Gemini Imagen, OpenAI gpt-image, Grok Imagine
- Video generation — Text-to-video and image-to-video (Gemini Veo 3.1, Grok Imagine Video)
- 3 providers x 2 tiers — Same interface for
gemini/openai/grokatpoor(cheap/fast) orrich(high quality); swap via parameter - Open model passthrough — Understanding routes through litellm; pass any
provider/model, or configure an ordered model chain (no hardcoded catalog) - Degraded mode — Server starts with zero credentials and surfaces remaining providers as you add keys
- Response cache — Disk-based caching of
understandresponses with configurable TTL - Dual transport — pure stdio with provider env vars (default) or HTTP multi-user with paste-token relay form
Install
Run with uvx (no install step) or pull the container image:
# uvx -- recommended, runs the published PyPI ...
