This server provides tools for generating AI images and videos using OpenAI and Google models, saving outputs locally.
OpenAI Image Generation (openai_generate_image): Generate 1–10 images from a text prompt using OpenAI Images API (default model: gpt-image-2). Customize size (e.g., 1024x1024, 1536x1024; dimensions divisible by 16), quality (low, medium, high), number of images, optional output filename, and model override via parameter or environment variable. Images are saved to assets/images/.
Google Gemini Image Generation (gemini_generate_image): Generate a single image from a text prompt using Gemini (default model: gemini-2.5-flash-image). Supports optional filename and model override. Images saved to assets/images/.
Google Veo Video Generation (gemini_generate_video): Generate a video from a text prompt using Veo via the Gemini API (default model: veo-3.1-generate-001). Supports optional negative_prompt, aspect_ratio (16:9 or 9:16), filename, model override (e.g., faster preview model), and automatic asynchronous polling until completion. Videos saved to assets/videos/.
Requirements: Requires user-provided OpenAI and Google Gemini API keys.
Provides image generation via Gemini's Nano Banana model and video generation via Veo through the Gemini API.
Provides image generation through OpenAI's Images API, supporting models such as gpt-image-2.
Click on "Deploy 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., "@image-video-mcp generate an image of a cat wearing a spacesuit"
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.
One MCP server that adds OpenAI image generation and Google Gemini image + video generation to Claude Code — and any MCP client. Bring your own API keys.
Tool | Provider | Default model |
| OpenAI Images API |
|
| Gemini (Nano Banana) |
|
| Veo via Gemini API |
|
Images save to assets/images/, videos to assets/videos/. Every model is
overridable with an env var, so it keeps working as models roll over.
Run this — it installs everything, asks for your two API keys, and registers the tools in both Claude Code and the Claude desktop app:
bash <(curl -fsSL https://raw.githubusercontent.com/eracom-technologies/image-video-mcp/main/setup.sh)That's it. Restart the Claude desktop app when it finishes, and the image/video
tools are ready in both places. (Prefer to set keys inline? OPENAI_API_KEY=sk-... GEMINI_API_KEY=... bash <(curl -fsSL .../setup.sh).)
The manual steps below are only if you'd rather do it yourself.
Related MCP server: media-gen-mcp
One-line, straight from git:
pip install "git+https://github.com/eracom-technologies/image-video-mcp.git"Or from a local clone:
git clone https://github.com/eracom-technologies/image-video-mcp.git
cd image-video-mcp && pip install .Both create the image-video-mcp command. Prefer isolation? pipx install .
or uv tool install ..
claude mcp add-json image-video '{
"command": "image-video-mcp",
"args": [],
"env": { "OPENAI_API_KEY": "sk-...", "GEMINI_API_KEY": "..." }
}'
claude mcp list # confirm it shows "image-video"Or drop the included .mcp.json into a project folder (Claude Code auto-detects
it). Get keys at platform.openai.com and
aistudio.google.com/apikey.
Ask Claude Code:
"Use gemini_generate_video: a slow drone shot over a misty forest, 16:9."
PYTHONPATH=src python scripts/smoke_test.py # offline: tools register
PYTHONPATH=src python scripts/smoke_test.py --live # real generations (uses credits)See README-MCP.md for OS-specific config locations, all environment variables, model/deprecation notes, sharing, and troubleshooting.
MIT — see LICENSE.
Generate an image with Google Gemini (Nano Banana) and save to assets/images.
Args: prompt: Text description of the image to generate. model: Override the model id (default from GEMINI_IMAGE_MODEL / gemini-2.5-flash-image). Newer option: a Nano Banana 2 / Gemini 3.x Flash Image id if enabled on your key. filename: Optional base filename (without extension).
Returns: A human-readable summary listing the saved file path(s).
Inputs: prompt (required), model, filename. Returns: result
Generate a video with Google Veo (via Gemini API) and save to assets/videos.
This is an async operation: the tool submits the job, polls until done, then downloads the result. It can take a few minutes.
Args: prompt: Text description of the video to generate. model: Override the model id (default from GEMINI_VIDEO_MODEL / veo-3.1-generate-001). Use veo-3.1-fast-generate-preview for speed. negative_prompt: Optional description of what to avoid. aspect_ratio: "16:9" or "9:16". filename: Optional base filename (without extension).
Returns: A human-readable summary with the saved video path.
Inputs: prompt (required), model, filename, aspect_ratio, +1 more. Returns: result
Generate image(s) with OpenAI's Images API and save them to assets/images.
Args: prompt: Text description of the image to generate. size: WIDTHxHEIGHT (e.g. "1024x1024", "1536x1024"). gpt-image-2 supports arbitrary sizes with width/height divisible by 16. quality: "low", "medium", or "high". n: Number of images (1-10 for gpt-image-2). model: Override the model id (default from OPENAI_IMAGE_MODEL / gpt-image-2). filename: Optional base filename (without extension) for a single image.
Returns: A human-readable summary listing the saved file path(s).
Inputs: prompt (required), n, size, model, +2 more. Returns: result
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 3 tools
The two image generation tools (openai_generate_image and gemini_generate_image) overlap in purpose, both generating an image from a prompt. The provider prefixes help distinguish them, but an agent may still be uncertain which to choose without explicit context. The video tool is clearly distinct.
All tool names follow the same [provider]_[verb]_[object] pattern with snake_case, making the set predictable and easy to navigate. The consistent use of 'generate' as the verb reinforces a clear convention.
Three tools is well-scoped for a media generation server covering image and video output. Each tool serves a distinct provider or modality, and the count is within the ideal range for a focused MCP.
The toolset covers image generation via OpenAI and Gemini, and video generation via Gemini, satisfying the core 'image-video' purpose. Minor gaps exist, such as no OpenAI video generation or image editing capabilities, but these are not essential given the apparent scope.
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