By imyousuf
MCP tools for Nano Banana image generation, Veo video generation, and FFmpeg stitching.
Iterative multi-agent workflow for turning a director's brief, movie bible, or chapter outline into structured movie production artifacts. Use when the user wants Codex to run staged agent-like movie development: brief ingestion, research, chapter structuring, screenplay drafting, continuity harmonization, visual prompt planning, Veo/Nano Banana generation planning, or iterative review gates before moving between stages.
Use the Storytelling Media MCP tools for Nano Banana image generation, Veo video generation, and FFmpeg video stitching.
MCP tools for an agent-driven video creation workflow.
This server gives Codex, Claude Code, or another MCP client access to:
Image generation defaults to the newer Google image models:
flash: gemini-3.1-flash-image also described by Google as Nano Banana 2.pro: gemini-3-pro-image also described by Google as Nano Banana Pro.legacy_flash: gemini-2.5-flash-image for older Nano Banana workflows.Video generation defaults to:
veo-3.1-generate-001python -m venv .venv
. .venv/bin/activate
pip install -e .
cp .env.template .env
FFmpeg must be installed and available on PATH for stitching.
The Gemini API can also use OAuth/ADC, but the ADC login must include Google's Gemini scope:
gcloud auth application-default login \
--scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.retriever'
gcloud auth application-default set-quota-project "$GOOGLE_CLOUD_PROJECT"
Then run the MCP server with GOOGLE_CLOUD_PROJECT set to the project that should be used for quota and billing:
python -m storytelling_mcp
The server also accepts GOOGLE_CLOUD_QUOTA_PROJECT or GCLOUD_PROJECT. Then call the image or video tools with auth_mode="adc". If your ADC token was created without the generative-language.retriever scope, the Gemini API returns ACCESS_TOKEN_SCOPE_INSUFFICIENT.
python -m storytelling_mcp
Example stdio server config using an API key:
{
"mcpServers": {
"storytelling-media": {
"command": "python",
"args": ["-m", "storytelling_mcp"],
"env": {
"GEMINI_API_KEY": "your_api_key"
}
}
}
}
Example stdio server config using ADC/Vertex:
{
"mcpServers": {
"storytelling-media": {
"command": "python",
"args": ["-m", "storytelling_mcp"],
"env": {
"GOOGLE_CLOUD_PROJECT": "your-gcp-project-id",
"GOOGLE_CLOUD_LOCATION": "us-central1"
}
}
}
}
This repo includes installable plugin metadata for both Claude Code and Codex.
Claude Code:
/plugin marketplace add imyousuf/storytelling-media-mcp
/plugin install storytelling-media-mcp@storytelling-media
Codex:
codex plugin marketplace add imyousuf/storytelling-media-mcp --sparse .agents/plugins --sparse plugins
Then open /plugins in Codex and install storytelling-media-mcp from the storytelling-media marketplace.
The plugin launcher uses STORYTELLING_MEDIA_MCP_ROOT for local development. If that variable is unset, it creates a plugin-local virtual environment and installs this package from GitHub before starting the MCP server.
The plugin also includes $movie-production-pipeline, a gated multi-agent workflow for turning a director's brief into stage-approved production artifacts. It expects user feedback at each stage before moving forward.
nano_banana_generate_image: text-to-image or text-and-image-to-image.veo_generate_video: text-to-video, image-to-video, interpolation, or reference-image-guided generation.stitch_videos: concatenate local MP4 clips with FFmpeg.Generated files are written to paths supplied by the caller.
Admin access level
Server config contains admin-level keywords
Requires secrets
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Needs API keys or credentials to function
Needs API keys or credentials to function
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