imagine-mcp

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

Image and video understanding + generation for AI agents -- across Gemini, OpenAI, and Grok.

Table of contents

• Features

• Install

• Configuration

• Documentation

• Tools

• Comparison

• Security

• Build from Source

• Deploy to Cloudflare

• Trust Model

• Contributing

• License

Related MCP server: mcp-media-engine

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

• 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

• 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 package
uvx imagine-mcp

# Docker
docker run -it --rm ghcr.io/n24q02m/imagine-mcp:latest

Add it to an MCP client by pointing the client at the uvx imagine-mcp command and supplying at least one provider key (see Configuration):

{
  "mcpServers": {
    "imagine": {
      "command": "uvx",
      "args": ["imagine-mcp"],
      "env": { "GEMINI_API_KEY": "AIza..." }
    }
  }
}

For per-client snippets (Claude Code, Codex, Gemini CLI, Cursor, Windsurf) and the browser-based HTTP setup, see the Setup docs.

Install with an AI agent -- paste this to your AI coding agent:

Install MCP server imagine-mcp following the steps at
https://raw.githubusercontent.com/n24q02m/claude-plugins/main/plugins/imagine-mcp/setup-with-agent.md

Configuration

Two transports (default stdio; opt into http with --http, MCP_TRANSPORT=http, or TRANSPORT_MODE=http):

• stdio (default) -- single-user, reads credentials from env vars only. Exits if none of the three provider keys are set.

• http -- HTTP daemon. Local self-host on 127.0.0.1 by default, or multi-user remote (per-JWT-sub credential isolation) when PUBLIC_URL + MCP_DCR_SERVER_SECRET are set. In HTTP mode credentials are entered through a browser form at /authorize.

Provider keys

All optional -- the server starts in degraded mode and surfaces whichever providers have a key. Set at least one.

When a tool is called without an explicit provider, the first key present wins in the order XAI_API_KEY -> OPENAI_API_KEY -> GEMINI_API_KEY.

Model chains (optional)

Override the built-in provider/tier catalog with explicit model chains. Each is a CSV of litellm provider/model entries; the order is the fallback order.

Understanding is routed through litellm (provider/model passthrough), so any litellm provider works -- supply that provider's <PROVIDER>_API_KEY. Generation stays on the native provider SDKs (Gemini, OpenAI, Grok). Example:

{
  "mcpServers": {
    "imagine": {
      "command": "uvx",
      "args": ["imagine-mcp"],
      "env": {
        "UNDERSTAND_MODELS": "gemini/gemini-3.1-pro-preview,openai/gpt-5.4",
        "GEMINI_API_KEY": "AIza...",
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Runtime knobs

config(action="set", key=..., value=...) adjusts log_level, default_provider, default_tier, and cache_ttl_seconds at runtime.

Documentation

Full docs at mcp.n24q02m.com/servers/imagine-mcp/setup/:

• Setup -- install methods for Claude Code, Codex, Gemini CLI, Cursor, Windsurf, mcp.json

• Modes overview -- stdio / local-relay / remote-relay / remote-oauth

• Multi-user setup -- per-JWT-sub credential model

Tools

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

Comparison

How imagine-mcp stacks up against direct competitors in each pillar:

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.

• Credential storage -- Credentials submitted through the browser credential form are stored encrypted via mcp-core (AES-GCM, machine-bound key) at ~/.imagine-mcp/config.json.

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 the server in stdio mode (add --http for the HTTP daemon)

Deploy to Cloudflare

Run your own imagine instance serverless on Cloudflare (Worker + Container + KV). Storage is KV-only -- the per-user credential vault lives in KV, and generation returns base64 only because the container filesystem is ephemeral (IMAGINE_OUTPUT_MODE=base64).

Prerequisites: a Cloudflare account on the Workers Paid plan -- required for Containers (the Cloudflare free tier does not include Containers) -- and the wrangler CLI.

1. git clone https://github.com/n24q02m/imagine-mcp && cd imagine-mcp

2. wrangler login

3. Create the KV namespace (imagine is KV-only -- no D1 or Vectorize), then paste the returned id into wrangler.jsonc (the <imagine-kv-namespace-id> placeholder):

   wrangler kv namespace create imagine-kv

4. Push the container image to your Cloudflare managed registry (CF Containers cannot pull from external registries directly), then set <YOUR_ACCOUNT_ID> in wrangler.jsonc:

   docker pull ghcr.io/n24q02m/imagine-mcp:beta
   docker tag ghcr.io/n24q02m/imagine-mcp:beta imagine-mcp:beta
   wrangler containers push imagine-mcp:beta   # prints registry.cloudflare.com/<ACCOUNT_ID>/imagine-mcp:beta

5. Point the remaining wrangler.jsonc placeholders at your own domain: <YOUR_PUBLIC_URL> (the vars.PUBLIC_URL, e.g. https://imagine.example.com) and <YOUR_WORKER_DOMAIN> (the routes custom-domain pattern, e.g. imagine.example.com).

6. Set secrets. CREDENTIAL_SECRET (stable JWT signing key + per-user vault key) and MCP_DCR_SERVER_SECRET (proof of an intentional multi-user deploy) are required; MCP_RELAY_PASSWORD gates the browser setup form's login. Provider keys are optional server defaults -- users normally paste their own through the setup form instead:

   wrangler secret put CREDENTIAL_SECRET
   wrangler secret put MCP_DCR_SERVER_SECRET
   wrangler secret put MCP_RELAY_PASSWORD
   wrangler secret put GEMINI_API_KEY       # optional provider default
   wrangler secret put OPENAI_API_KEY       # optional provider default
   wrangler secret put XAI_API_KEY          # optional provider default

7. wrangler deploy, then open your Worker domain and finish setup in the browser relay form.

The http container image already runs multi-user (MCP_TRANSPORT=http is baked into the image target). Storage maps to Cloudflare via MCP_STORAGE_BACKEND=cf-kv (encrypted credential vault) with IMAGINE_OUTPUT_MODE=base64, which forces base64 responses so no media path is written to the ephemeral container filesystem.

Trust Model

This plugin implements TC-Local (machine-bound, single trust principal). See mcp-core trust model for full classification.

Contributing

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

License

MIT -- see LICENSE.