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Google Flow MCP (V2 Controller)

Self-hosted Model Context Protocol (MCP) server for Google Flow (labs.google/fx/tools/flow), refactored into a lean controller for book and story asset generation pipelines.

Authenticate with your own Google account to generate character candidates, props, places, scenes, and video clips—saving all outputs directly to your local file system or external drive (/Volumes/Xstorage/...).


Production Workflow Architecture

This MCP operates strictly as a controller for Google Flow asset creation:

Approved dossier
→ create book project
→ Storyboard Studio
→ characters / places / props / scenes
→ download candidates
→ approve stills
→ Veo / Omni animation
→ download clips
→ MiniMax
→ Remotion / CapCut
NOTE

This MCP server is a thin Google Flow controller layer. It doesNOT handle full dossier truth systems, final trailer scripting strategy, MiniMax orchestration, Remotion assembly, CapCut rendering, or video publishing. Those operations belong outside this repository in the primary Immerse pipeline.


Related MCP server: Google Veo 3.1 MCP Server

Key Principles & Scope

  • 🎯 Google Flow Controller: Pure control layer for authenticating, listing/creating projects, executing Storyboard Studio/custom tools, generating media, and downloading assets locally.

  • 🔒 Spend Guard & Budget Safety: Video generation tools (generate_video and generate_video_from_image) strictly require confirm_spend: true to prevent accidental credit consumption. Supports max_credits and max_generations limits.

  • 📂 Local Storage Integration: Auto-scaffolds clean book folder structures (/characters, /props, /places, /scenes, /clips) on local or external drives.

  • 🤝 Pipeline Handoff: Decoupled from final trailer editing or assembly. Assets are saved cleanly for handoff to external tools (MiniMax, Remotion, CapCut).

  • 🔐 Isolated Auth & Safety: Session credentials are kept strictly isolated and stored locally with restricted 0600 file permissions.


Installation & Setup

1. Build Server

git clone https://github.com/LeoSzn12/google-flow-mcp.git
cd google-flow-mcp
npm install
npm run build

2. Environment Configuration

Copy .env.example to .env:

cp .env.example .env

Set your session credentials and local output root:

GOOGLE_COOKIES="your_google_cookies_string"
LOCAL_STORAGE_ROOT="/Volumes/Xstorage/Media"

Available MCP Tools (V2)

Core Control Tools (14)

Tool Name

Description

Key Parameters

flow_status

Check authentication state & account details

account

connect_google_account

Save Google auth cookies or tokens

cookies, bearerToken

list_projects

List Google Flow projects on your account

-

create_project

Create a new Google Flow project

name

list_tools

List custom tools in a project

project_id

run_custom_tool

Execute custom tool or Storyboard tool

tool_id, project_id, prompt, inputs

generate_image

Generate image via NARWHAL / Nano Banana

prompt, model, aspect, output_folder

generate_images_batch

Batch generate image candidates

prompts, model, aspect, output_folder

generate_video

Text-to-video via Veo 3.1 (Spend Guard)

prompt, confirm_spend (required), max_credits, max_generations, output_folder

generate_video_from_image

Image-to-video keyframe animation (Spend Guard)

prompt, start_image_url, confirm_spend (required), max_credits, max_generations, output_folder

check_job_status

Query status of async generation jobs

job_id, project_id

save_media_to_local_folder

Save generated media URL or ID to local disk

media_url_or_id, output_folder, filename

list_media

List all generated media items in session memory

-

get_media_details

Get details for a specific media item

media_id

Thin Helper Tools (2)

Tool Name

Description

Key Parameters

create_book_project

Scaffold local book folder structure on disk

book_title, local_storage_root

run_storyboard_studio_from_dossier

Execute Storyboard Studio with dossier input

dossier_text, book_title, asset_types, output_folder


Integration Guides

Claude Code / Codex

claude mcp add --transport stdio google-flow -- node /absolute/path/to/google-flow-mcp/dist/index.js

Cursor / Windsurf / Cline (mcpServers)

Add to ~/.cursor/mcp.json or .vscode/mcp.json:

{
  "mcpServers": {
    "google-flow": {
      "command": "node",
      "args": ["/absolute/path/to/google-flow-mcp/dist/index.js"],
      "env": {
        "GOOGLE_COOKIES": "your_google_cookies_string_here",
        "LOCAL_STORAGE_ROOT": "/Volumes/Xstorage/Media"
      }
    }
  }
}

Happy Path Example Workflow (The Odyssey)

Here is how to run a complete asset pipeline workflow for an example book (The Odyssey):

1. Create a Book Project & Local Storage Folders

Call create_book_project:

{
  "book_title": "The Odyssey",
  "local_storage_root": "/Volumes/Xstorage/Media"
}

Creates /Volumes/Xstorage/Media/the-odyssey/ with subfolders: characters/, props/, places/, scenes/, clips/.

2. Run Storyboard Studio with an Approved Dossier

Call run_storyboard_studio_from_dossier:

{
  "book_title": "The Odyssey",
  "dossier_text": "Odysseus: Weathered ancient Greek king and mariner, dark curly hair, bearded, wearing bronze-trimmed linen tunic, standing on rocky shore looking out at Aegean Sea.",
  "asset_types": ["characters", "places", "scenes"],
  "output_folder": "/Volumes/Xstorage/Media"
}

Generates visual candidates and downloads stills directly into local project subfolders.

3. Animate Approved Keyframes into Video Clips

Call generate_video_from_image with explicit spend authorization:

{
  "prompt": "Slow panning shot across Aegean sea waves crashing on rocky cliffs behind Odysseus",
  "start_image_url": "/Volumes/Xstorage/Media/the-odyssey/characters/odysseus_still.png",
  "confirm_spend": true,
  "max_credits": 20,
  "max_generations": 1,
  "output_folder": "/Volumes/Xstorage/Media/the-odyssey/clips"
}

Renders video using Google Veo 3.1 and saves flow_video_<timestamp>.mp4 to /clips.

4. Immerse Production Handoff

Hand off downloaded .png stills and .mp4 video clips to external assembly tools (MiniMax, Remotion, CapCut).


License

MIT

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