Google AI Studio MCP Server

A universal, open-source Model Context Protocol (MCP) server that exposes the granular features of Google AI Studio (Gemini, Imagen, Veo) directly to your code editors and local LLM clients.

Features

• Full Model Support: Use any Google AI Studio model your API key has access to (e.g., gemini-2.5-pro, gemini-2.5-flash).

• Granular Imagen Controls: Control Aspect Ratio, Number of Images, Output Format, and Person Generation settings.

• Veo Video Generation: Built-in support for Veo (requires approved API key).

• Cross-Editor Compatibility: Works out of the box with VS Code, Cursor, Cloud Code, Claude Desktop, and local Ollama clients.

Important Limitations & Versioning

• Music Generation is NOT supported: Music generation models (like Lyria or MusicFX) are currently locked behind Google's web UI and private Vertex endpoints. They are not exposed via the standard developer API.

• Model Versioning (-latest vs Hardcoding): For Text and Multimodal models (like Flash and Pro), you can pass the -latest aliases (e.g., gemini-flash-latest, gemini-pro-latest). This ensures the server naturally pulls updated models without breaking when older versions are deprecated. Note that older families such as gemini-1.5-* have been retired and may 404 on newer keys.

  • CRITICAL EXCEPTION: Video models (Veo) do not support the -latest suffix. If Veo video generation stalls out or throws a "Model Not Found" error, you must manually update the hardcoded numerical string in the source code (e.g., bump from veo-3.1-generate-preview to veo-4.0-generate-001).

Related MCP server: NotebookLM MCP Server

Installation

Prerequisites

• Node.js (v18+)

• A Google AI Studio API Key

Setup

1. Clone this repository:

   git clone https://github.com/yourusername/google-ai-studio-mcp.git
   cd google-ai-studio-mcp

2. Install dependencies:

   npm install

3. Build the server:

   npm run build

Note: For security, do not use a .env file. You will inject your API key directly into your editor's MCP configuration as shown below.

Configuration

Inject your API key directly into your editor's protected global MCP configuration file.

Antigravity / Cloud Code

Use the following JSON snippet in your mcp_config.json:

"google-ai-studio": {
  "command": "node",
  "args": ["C:/GitHub/google-ai-studio-mcp/dist/index.js"],
  "env": {
    "GEMINI_API_KEY": "YOUR_ACTUAL_API_KEY"
  }
}

Cursor / VS Code

Add to your settings:

{
  "mcp.servers": {
    "google-ai-studio": {
      "command": "node",
      "args": ["/path/to/google-ai-studio-mcp/dist/index.js"]
    }
  }
}

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "google-ai-studio": {
      "command": "node",
      "args": ["/path/to/google-ai-studio-mcp/dist/index.js"]
    }
  }
}

Tools Exposed

• googleaistudio_generate_content: Text generation with full parameter support (Temperature, System Instructions, Thinking Level).

• googleaistudio_generate_image: Image generation (Imagen) with Aspect Ratio, Number of Images, MIME Type, and Person Generation controls. Returns a native inline image plus the saved file path(s).

• googleaistudio_start_video: Starts a Veo video generation and immediately returns an Operation ID (non-blocking, avoids MCP execution timeouts).

• googleaistudio_check_video: Polls an Operation ID; when generation is done it downloads the MP4 to disk and returns the path. Poll every 30–60 seconds. Polling is durable — the Operation ID can be re-checked even after a server restart.

• googleaistudio_manage_outputs: File management tool to prevent hard drive bloat in a local AI Outputs directory. Actions: clear_all, clear_older_than_two_weeks, get_folder_path (subdirectories are skipped, not deleted).

Where media is saved: saveDirectory & artifactDirectory

Media tools take an explicit, AI-supplied absolute path so generated assets land exactly where you want them:

• saveDirectory (required on generate_image / check_video, targetDirectory on manage_outputs): the absolute local directory to save the file into. The directory is created if it doesn't exist. E.g. C:\Users\Name\Downloads or a project repo folder.

• artifactDirectory (optional): the current conversation's brain/artifact directory, e.g. <appDataDir>\brain\<conversation-id>. When provided, the MCP writes a second copy of the media there so the host chat UI (e.g. Antigravity's Artifact renderer, which only renders files physically inside the brain directory) can display it inline. Images are also returned as a native MCP image block; videos rely on this copy since MCP has no native video block.

Rule of thumb for agents: pass saveDirectory for the durable/project copy, and pass artifactDirectory (your conversation's brain dir) whenever you want the user to see the media inline in chat.

Development: Dual-Environment Build & Deploy

This server is developed in the public repo but mounted by the local agent from its config directory, so changes must be built and copied across:

1. Edit source in the GitHub repo (e.g. C:\GitHub\google-ai-studio-mcp).

2. npm run build.

3. Force-copy the built dist/ into the active agent config path (e.g. C:\Users\<you>\.gemini\config\google-ai-studio-mcp).

4. Hard-restart the MCP server so the agent re-mounts the updated tools.