by eternnoir
Leverage Google AI Studio & Gemini API to process images, videos, audio, PDFs, & text for document conversion, analysis
Connects to Google AI Studio/Gemini API to generate and analyze content from text, images, videos, audio, PDFs, and other file formats. Requires a Google AI Studio API key.
Google AI Studio is a community-built MCP server published by eternnoir that provides AI assistants with tools and capabilities via the Model Context Protocol. Leverage Google AI Studio & Gemini API to process images, videos, audio, PDFs, & text for document conversion, analysis It is categorized under ai ml.
You can install Google AI Studio in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Google AI Studio is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Google AI Studio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Google AI Studio is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We wired Google AI Studio into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Google AI Studio is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Google AI Studio is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Google AI Studio reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
According to our notes, Google AI Studio benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Google AI Studio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Google AI Studio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
According to our notes, Google AI Studio benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
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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.
GEMINI_API_KEY=your_api_key npx -y aistudio-mcp-server
npm install -g aistudio-mcp-server
GEMINI_API_KEY=your_api_key aistudio-mcp-server
Set your Google AI Studio API key as an environment variable:
export GEMINI_API_KEY=your_api_key_here
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
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
path or contentpath (string): Path to filecontent (string): Base64 encoded file contenttype (string, optional): MIME type (auto-detected from file extension)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 onesSupported file types (Gemini 2.5 models):
File limitations:
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"}
]
}
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"
}
]
}
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"
}
]
}
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"
}
]
}
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"
}
}
}
}
Make sure you have Node.js 20.0.0 or higher installed.
npm install
npm run build
GEMINI_API_KEY=your_api_key npm run dev
MIT
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.