media-mcp 0.2.1

MCP server exposing Google Gemini media generation APIs as tools for AI agents

media-mcp

MCP server for AI-powered media generation using Google Gemini. Generate images, videos, music, and speech directly from your AI agent.

Features

• Image Generation — Create and edit images using Gemini's Nano Banana models with support for multiple aspect ratios, resolutions up to 4K, and reference images
• Video Generation — Generate videos with native audio, dialogue, and sound effects using Veo models (text-to-video, image-to-video, video extension)
• Music Generation — Create instrumental music with weighted text prompts using Lyria RealTime (genre, instrument, mood control with BPM and scale)
• Speech Generation — Convert text to speech with voice selection, multi-speaker support, and natural language style control using Gemini TTS

Installation

Using uvx (recommended)

uvx media-mcp

Using pip

pip install media-mcp

Prerequisites

• Python 3.10+
• A Google Gemini API key

Configuration

Set your Gemini API key as an environment variable:

export GEMINI_API_KEY="your-gemini-api-key"

Environment Variables

Variable	Required	Description
GEMINI_API_KEY	Yes	Google Gemini API key for authentication
MEDIA_OUTPUT_DIR	No	Directory path for saving generated media files (see below)

Output behavior

When MEDIA_OUTPUT_DIR is set, every generated file is saved to that directory and the tool returns only the file path — no binary data is included in the response. This is the recommended setup because MCP messages are stored in the conversation history, and large base64 payloads pollute context and waste tokens.

When MEDIA_OUTPUT_DIR is not set, the server has no filesystem target, so it returns the raw base64-encoded data directly in the response. This works for quick experiments but is not recommended for production use.

MCP Client Setup

Claude Desktop

Add to your claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "media-mcp": {
      "command": "uvx",
      "args": ["media-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key",
        "MEDIA_OUTPUT_DIR": "/path/to/media/output"
      }
    }
  }
}

Claude Code

claude mcp add media-mcp --transport stdio -- uvx media-mcp

Or add manually to .mcp.json:

{
  "mcpServers": {
    "media-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": ["media-mcp"],
      "env": {
        "GEMINI_API_KEY": "${GEMINI_API_KEY}",
        "MEDIA_OUTPUT_DIR": "/path/to/media/output"
      }
    }
  }
}

Tools

generate_image

Generate or edit images using Gemini's Nano Banana models.

Parameter	Type	Required	Default	Description
prompt	string	Yes	—	Text description of the image to generate
model	enum	No	nano-banana-2	nano-banana-2, nano-banana-pro, nano-banana
aspect_ratio	enum	No	1:1	1:1, 9:16, 16:9, 3:2, 4:3, and more
image_size	enum	No	1K	512px, 1K, 2K, 4K
reference_images	list[str]	No	—	Base64-encoded reference images
thinking_level	enum	No	minimal	minimal, high
use_google_search	bool	No	false	Enable Google Search grounding

Example prompt: "A watercolor painting of a cozy cabin in the mountains during autumn"

generate_video

Generate videos with native audio using Veo models.

Parameter	Type	Required	Default	Description
prompt	string	Yes	—	Text description including dialogue, sound effects, camera directions
model	enum	No	veo-3.1	veo-3.1, veo-3
aspect_ratio	enum	No	16:9	16:9 (landscape), 9:16 (portrait)
resolution	enum	No	—	720p, 1080p, 4K
first_frame_image	str	No	—	Base64-encoded image for first frame
last_frame_image	str	No	—	Base64-encoded image for last frame
reference_images	list[str]	No	—	Up to 3 base64-encoded reference images

Example prompt: "A slow dolly shot through a neon-lit alley at night, rain falling, 'Where are you going?' whispered softly, footsteps echoing"

generate_music

Generate instrumental music using Lyria RealTime with weighted prompts.

Parameter	Type	Required	Default	Description
prompts	list[dict]	Yes	—	Weighted prompts, e.g. [{"text": "minimal techno", "weight": 1.0}]
bpm	int	No	—	Tempo in beats per minute
temperature	float	No	1.0	Randomness/creativity control
scale	str	No	—	Musical scale constraint (e.g. C_MAJOR_A_MINOR)
duration_seconds	int	No	30	Duration of the output clip

Example prompts: [{"text": "Piano", "weight": 2.0}, {"text": "Meditation", "weight": 0.5}]

generate_speech

Convert text to speech with voice and style control.

Parameter	Type	Required	Default	Description
text	string	Yes	—	Text to speak. For multi-speaker, format as dialogue with speaker names.
model	enum	No	flash-tts	flash-tts, pro-tts
voice_name	str	No	—	Voice name: Kore, Puck, Charon, Fenrir, Aoede, Leda, Orus, Zephyr
multi_speaker	bool	No	false	Enable multi-speaker mode
speakers	list[dict]	No	—	Speaker-to-voice mapping, e.g. [{"name": "Alice", "voice_name": "Kore"}]
style_instructions	str	No	—	Style guidance, e.g. "Read in a calm, slow pace"

Example: Text: "Welcome to the show!" with voice_name: "Kore" and style_instructions: "Say cheerfully"

Troubleshooting

"GEMINI_API_KEY environment variable is not set"

Set the environment variable before starting the server:

export GEMINI_API_KEY="your-key-here"

When using Claude Desktop or Claude Code, pass the key via the env block in your MCP configuration (see MCP Client Setup).

"Authentication failed" or 401 errors

Your API key may be invalid or expired. Verify it at Google AI Studio.

"Rate limit or quota exceeded" or 429 errors

Wait a moment and retry. Check your API quota at Google AI Studio.

"Content blocked by safety filter"

Modify your prompt to avoid restricted content. The Gemini API applies safety filters to all generated media.

Python version errors

media-mcp requires Python 3.10 or later. Check your version:

python --version

License

MIT