mordor-forge/gemini-media-mcp
by Various
Unified Go MCP server for AI media generation via Google Gemini API and Vertex AI
MCP
mordor-forge/gemini-media-mcp
Added 1 June 2026
Overview
A Go-based MCP server that provides a unified interface for generating images, audio, and video using Google Gemini API and Vertex AI. It acts as a bridge between AI agents and Gemini's media generation capabilities through the Model Context Protocol.
Best for
Best for
Developers building AI agent workflows that need to generate images, audio, or video through Google's Gemini models.
Use cases
- Generate images and audio with an AI agent via Gemini
- Integrate Vertex AI media generation into MCP-compatible tools
- Build custom AI workflows that create multimedia content programmatically
How to use
Install
go install github.com/mordor-forge/gemini-media-mcp/cmd/gemini-media-mcp@latest Tools exposed
generate_imageedit_imagecompose_imagesgenerate_videoanimate_imageextend_videovideo_statusdownload_videogenerate_audiogenerate_musiclist_modelsget_config
Tested with
Claude Code
Example client config
{\n "mcpServers": {\n "gemini-media": {\n "command": "gemini-media-mcp",\n "env": {\n "GOOGLE_API_KEY": "your-api-key",\n "MEDIA_OUTPUT_DIR": "/path/to/output"\n }\n }\n }\n} Notes
A Go-based MCP server that provides a unified interface for generating images, audio, and video using Google Gemini API and Vertex AI. It acts as a bridge between AI agents and Gemini’s media generation capabilities through the Model Context Protocol.
5 stars on GitHub. Last updated 2026-04-15. Licensed Apache-2.0.
Use cases
- Generate images and audio with an AI agent via Gemini
- Integrate Vertex AI media generation into MCP-compatible tools
- Build custom AI workflows that create multimedia content programmatically
Pros
- Written in Go for efficient performance
- Supports both Gemini API and Vertex AI for flexibility
- Unified single server for multiple media types
Cons
- Limited to Google Gemini ecosystem, no support for other model providers
- Small community with only 5 GitHub stars may mean limited support
- Requires separate Google Cloud credentials for Vertex AI access
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Written in Go for efficient performance
- Supports both Gemini API and Vertex AI for flexibility
- Unified single server for multiple media types
Cons
- Limited to Google Gemini ecosystem, no support for other model providers
- Small community with only 5 GitHub stars may mean limited support
- Requires separate Google Cloud credentials for Vertex AI access
Pairs with
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