About Google AI Studio MCP Server
AI Studio MCP Server is an MCP server that connects to the Google AI Studio / Gemini API to generate content. It accepts files, system prompts and generation settings from MCP clients, and is aimed at users who need multimodal file processing within MCP workflows.
Key features
- 01Generates content with Gemini 2.5 models
- 02Accepts images, video, audio, PDF and text files
- 03Takes files by local path or Base64 content
- 04Supports system prompts and temperature settings
- 05Configurable model, timeout and file limits via environment variables
Use cases
- 01Converting PDF files to Markdown
- 02Analysing images, screenshots or diagrams
- 03Transcribing audio recordings
MCP support
The project is an MCP server that exposes a generate_content tool to MCP clients for calling Gemini.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
npx -y aistudio-mcp-serverGEMINI_API_KEYSecretGEMINI_MODELGEMINI_TIMEOUTGEMINI_MAX_OUTPUT_TOKENSSecretGEMINI_MAX_FILESGEMINI_MAX_TOTAL_FILE_SIZEGEMINI_TEMPERATURELocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
1Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| files | array | No | Array of files to include in generation (optional). Supports images, video, audio, PDFs, and text files. |
| model | string | No | Gemini model to use (optional) |
| only_image | boolean | No | When true, only return images without text (requires enable_image_generation to be true) |
| temperature | number | No | Temperature for generation (0-2, default 0.2) |
| user_prompt | string | Yes | User prompt for generation |
| system_prompt | string | No | System prompt to guide the AI behavior (optional) |
| image_save_path | string | No | Optional directory path to save generated images. When provided, images are saved to disk and data URIs are NOT returned to reduce response size. When not provided, images are returned as base64 data URIs only |
| thinking_budget | number | No | Thinking budget for models that support thinking mode (-1 for unlimited) |
| enable_google_search | boolean | No | Enable Google search capability for the model |
| enable_code_execution | boolean | No | Enable code execution capability for the model |
| enable_image_generation | boolean | No | Enable image generation capability (requires compatible model like gemini-2.5-flash-image-preview) |
Details
From the project READMEAI Studio MCP Server
A Model Context Protocol (MCP) server that integrates with Google AI Studio / Gemini API, providing content generation capabilities with support for files, conversation history, and system prompts.
Installation and Usage
Prerequisites
- Node.js 20.0.0 or higher
- Google AI Studio API key
Using npx (Recommended)
GEMINI_API_KEY=your_api_key npx -y aistudio-mcp-server
Local Installation
npm install -g aistudio-mcp-server
GEMINI_API_KEY=your_api_key aistudio-mcp-server
Configuration
Set your Google AI Studio API key as an environment variable:
export GEMINI_API_KEY=your_api_key_here
Optional Configuration
GEMINI_MODEL: Gemini model to use (default: gemini-2.5-flash)GEMINI_TIMEOUT: Request timeout in milliseconds (default: 300000 = 5 minutes)GEMINI_MAX_OUTPUT_TOKENS: Maximum output tokens (default: 8192)GEMINI_MAX_FILES: Maximum number of files per request (default: 10)GEMINI_MAX_TOTAL_FILE_SIZE: Maximum total file size in MB (default: 50)GEMINI_TEMPERATURE: Temperature for generation (0-2, default: 0.2)
Example:
export GEMINI_API_KEY=your_api_key_here
export GEMINI_MODEL=gemini-2.5-flash
export GEMINI_TIMEOUT=600000 # 10 minutes
export GEMINI_MAX_OUTPUT_TOKENS=16384 # More output tokens
export GEMINI_MAX_FILES=5 # Limit to 5 files per request
export GEMINI_MAX_TOTAL_FILE_SIZE=100 # 100MB limit
export GEMINI_TEMPERATURE=0.7 # More creative responses
Available Tools
generate_content
Generates content using Gemini with comprehensive support for files, conversation history, and system prompts. Supports various file types including images, PDFs, Office documents, and text files.
Parameters:
user_prompt(string, required): User prompt for generationsystem_prompt(string, optional): System prompt to guide AI behaviorfiles(array, optional): Array of files to include in generation- Each file object must have either
pathorcontent path(string): Path to filecontent(string): Base64 encoded file contenttype(string, optional): MIME type (auto-detected from file extension)
- Each file object must have either
model(string, optional): Gemini model to use (default: gemini-2.5-flash)temperature(number, optional): Temperature for generation (0-2, default: 0.2). Lower values produce more focused responses, higher values more creative ones
Supported file types (Gemini 2.5 models):
- Images: JPG, JPEG, PNG, GIF, WebP, SVG, BMP, TIFF
- Video: MP4, AVI, MOV, WEBM, FLV, MPG, WMV (up to 10 files per request)
- Audio: MP3, WAV, AIFF, AAC, OGG, FLAC (up to 15MB per file)
- Documents: PDF (treated as images, one page = one image)
- Text: TXT, MD, JSON, XML, CSV, HTML
File limitations:
- Maximum file size: 15MB per audio/video/document file
- Maximum total request size: 20MB (2GB when using Cloud Storage)
- Video files: Up to 10 per request
- PDF files follow image pricing (one page = one image)
Basic example:
{
"user_prompt": "Analyze this image and describe what you see",
"files": [
{
"path": "/path/to/image.jpg"
}
]
}
PDF to Markdown conversion:
{
"user_prompt": "Convert this PDF to well-formatted Markdown, preserving structure and formatting. Return only the Markdown content.",
"files": [
{
"path": "/path/to/document.pdf"
}
]
}
With system prompt:
{
"system_prompt": "You are a helpful document analyst specialized in technical documentation",
"user_prompt": "Please provide a detailed explanation of the authentication methods shown in this document",
"files": [
{"path": "/api-docs.pdf"}
]
}
Multiple files example:
{
"user_prompt": "Compare these documents and images",
"files": [
{"path": "/document.pdf"},
{"path": "/chart.png"},
{"content": "base64encodedcontent", "type": "image/jpeg"}
]
}
Common Use Cases
PDF to Markdown Conversion
To convert PDF files to Markdown format, use the generate_content tool with an appropriate prompt:
{
"user_prompt": "Convert this PDF to well-formatted Markdown, preserving structure, headings, lists, and formatting. Include table of contents if the document has sections.",
"files": [
{
"path": "/path/to/document.pdf"
}
]
}
Image Analysis
Analyze images, charts, diagrams, or photos with detailed descriptions:
{
"system_prompt": "You are an expert image analyst. Provide detailed, accurate descriptions of visual content.",
"user_prompt": "Analyze this image and describe what you see. Include details about objects, people, text, colors, and composition.",
"files": [
{
"path": "/path/to/image.jpg"
}
]
}
For screenshots or technical diagrams:
{
"user_prompt": "Describe this system architecture diagram. Explain the components and their relationships.",
"files": [
{
"path": "/architecture-diagram.png"
}
]
}
Audio Transcription
Generate transcripts from audio files:
{
"system_prompt": "You are a professional transcription service. Provide accurate, well-formatted transcripts.",
"user_prompt": "Please transcribe this audio file. Include speaker identification if multiple speakers are present, and format it with proper punctuation and paragraphs.",
"files": [
{
"path": "/meeting-recording.mp3"
}
]
}
For interview or meeting transcripts:
{
"user_prompt": "Transcribe this interview and provide a summary of key points discussed.",
"files": [
{
"path": "/interview.wav"
}
]
}
MCP Client Configuration
Add this server to your MCP client configuration:
{
"mcpServers": {
"aistudio": {
"command": "npx",
"args": ["-y", "aistudio-mcp-server"],
"env": {
"GEMINI_API_KEY": "your_api_key_here",
"GEMINI_MODEL": "gemini-2.5-flash",
"GEMINI_TIMEOUT": "600000",
"GEMINI_MAX_OUTPUT_TOKENS": "16384",
"GEMINI_MAX_FILES": "10",
"GEMINI_MAX_TOTAL_FILE_SIZE": "50",
"GEMINI_TEMPERATURE": "0.2"
}
}
}
}
Development
Setup
Make sure you have Node.js 20.0.0 or higher installed.
npm install
npm run build
Running locally
GEMINI_API_KEY=your_api_key npm run dev
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.