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MCP server for image/video understanding & generation (Gemini/OpenAI/Grok)

Project description

imagine-mcp

mcp-name: io.github.n24q02m/imagine-mcp

Production-grade MCP server for image and video understanding + generation across Gemini, OpenAI, and Grok.

CI codecov PyPI Docker License: MIT

Python FastMCP MCP semantic-release Renovate

imagine-mcp server

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 / grok at poor (cheap/fast) or rich (high quality); swap via parameter
  • Leaderboard-ranked models -- Provider ordering auto-refreshed weekly from Artificial Analysis + LMArena leaderboards
  • Zero-config onboarding -- Browser-based credential relay form; no .env files or manual credential plumbing
  • Degraded mode -- Server starts with zero credentials and surfaces remaining providers as you add keys
  • Response cache -- Disk-based caching of understand responses with configurable TTL
  • Smart stdio proxy -- stdio transport spawns a local HTTP daemon and forwards JSON-RPC frames, sharing credentials across invocations

Setup

With AI Agent -- copy and send this to your AI agent:

Please set up imagine-mcp for me. Follow this guide: https://raw.githubusercontent.com/n24q02m/imagine-mcp/main/docs/setup-with-agent.md

Manual setup -- follow docs/setup-manual.md

Tools

Tool Actions Description
understand -- Describe or reason over one or more image/video URLs. media_urls: list[str], prompt: str, provider, tier, max_tokens.
generate -- Generate an image or video from a text prompt. media_type: image|video, optional reference_image_url, optional job_id (video poll), aspect_ratio, duration_seconds.
config open_relay, relay_status, relay_skip, relay_reset, relay_complete, warmup, status, set, cache_clear Credential + runtime config: open relay form, check credential state, set runtime knobs (log level, default provider, TTL), clear response cache.
help -- Full Markdown documentation for understand, generate, or config topics.

Model IDs per provider x action x tier are leaderboard-ranked; see docs/models.md (auto-regenerated from src/imagine_mcp/models.py).

Security

  • SSRF + LFI prevention -- All media_urls and reference_image_url are validated at the dispatch boundary; only http:// and https:// schemes reach the providers. file://, ftp://, gopher://, and scheme-less URLs are rejected.
  • No credentials in errors -- Provider-side errors are sanitized before being returned.
  • Degraded start -- Missing credentials do not prevent the server from starting; affected actions surface actionable errors instead of crashing at boot.
  • Relay transport -- Credentials submitted through the local relay form are stored encrypted via mcp-core (config.enc, user-scoped platformdirs).

Build from Source

git clone https://github.com/n24q02m/imagine-mcp.git
cd imagine-mcp
mise run setup      # or: uv sync --group dev
mise run dev        # run http local relay daemon

Trust Model

This plugin implements TC-Local (machine-bound, single trust principal). See mcp-core/docs/TRUST-MODEL.md for full classification.

Mode Storage Encryption Who can read your data?
stdio (default) ~/.imagine-mcp/config.json AES-GCM, machine-bound key Only your OS user (file perm 0600)
HTTP self-host Same as stdio Same Only you (admin = user)

Contributing

See CONTRIBUTING.md for the full development workflow, commit convention, and release process. Issues + Discussions welcome.

License

MIT -- see LICENSE.

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