ai-ml

Google AI Studio

eternnoir

by eternnoir

Leverage Google AI Studio & Gemini API to process images, videos, audio, PDFs, & text for document conversion, analysis

Integrates with Google AI Studio/Gemini API to process multimodal content including images, videos, audio, PDFs, and text files for content generation, analysis, and document conversion tasks.

github stars

26

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Multimodal support for 10+ file typesConfigurable file size and count limitsConversation history support

best for

  • / Content creators analyzing multimedia assets
  • / Document processing and analysis workflows
  • / Developers building AI-powered applications
  • / Research teams working with mixed media content

capabilities

  • / Generate text content using Gemini models
  • / Analyze images, videos, and audio files
  • / Process PDF and Office documents
  • / Set custom system prompts for AI behavior
  • / Handle multiple files in a single request
  • / Configure model parameters like temperature and output tokens

what it does

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.

about

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.

how to install

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.

license

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.

readme

AI 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 generation
  • system_prompt (string, optional): System prompt to guide AI behavior
  • files (array, optional): Array of files to include in generation
    • Each file object must have either path or content
    • path (string): Path to file
    • content (string): Base64 encoded file content
    • type (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 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

FAQ

What is the Google AI Studio MCP server?
Google AI Studio is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Google AI Studio?
This profile displays 73 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

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

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

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

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ 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.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.573 reviews
  • Isabella Verma· Dec 28, 2024

    Google AI Studio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Kiara Huang· Dec 24, 2024

    Google AI Studio is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Kaira Yang· Dec 16, 2024

    We wired Google AI Studio into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Daniel Flores· Dec 16, 2024

    Google AI Studio is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Ishan Zhang· Nov 19, 2024

    Google AI Studio is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Zaid Martinez· Nov 15, 2024

    Google AI Studio reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Sofia Wang· Nov 7, 2024

    According to our notes, Google AI Studio benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Amina Sharma· Nov 7, 2024

    Google AI Studio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Kiara Kim· Oct 26, 2024

    Google AI Studio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Amina Martin· Oct 26, 2024

    According to our notes, Google AI Studio benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

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