storytelling-media-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@storytelling-media-mcpGenerate a 5-second video of a sunset over mountains."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Storytelling Media MCP
MCP tools for an agent-driven video creation workflow.
This server gives Codex, Claude Code, or another MCP client access to:
Nano Banana image generation and editing through Google's GenAI SDK.
Veo 3.1 video generation through Google's GenAI SDK.
Local video stitching through FFmpeg.
Models
Image generation defaults to the newer Google image models:
flash:gemini-3.1-flash-imagealso described by Google as Nano Banana 2.pro:gemini-3-pro-imagealso described by Google as Nano Banana Pro.legacy_flash:gemini-2.5-flash-imagefor older Nano Banana workflows.
Video generation defaults to:
veo-3.1-generate-001
Related MCP server: Video & Audio Editing MCP Server
Setup
python -m venv .venv
. .venv/bin/activate
pip install -e .
cp .env.template .envFFmpeg must be installed and available on PATH for stitching.
ADC Instead of API Key
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_mcpThe 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.
Run
python -m storytelling_mcpMCP Client Config
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"
}
}
}
}Agent Plugins
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-mediaCodex:
codex plugin marketplace add imyousuf/storytelling-media-mcp --sparse .agents/plugins --sparse pluginsThen 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.
Tools
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.
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