AI Vision MCP Server

A powerful Model Context Protocol (MCP) server that provides AI-powered image and video analysis using Google Gemini and Vertex AI models.

Features

• Dual Provider Support: Choose between Google Gemini API and Vertex AI

• Multimodal Analysis: Support for both image and video content analysis

• Flexible File Handling: Upload via multiple methods (URLs, local files, base64)

• Storage Integration: Built-in Google Cloud Storage support

• Comprehensive Validation: Zod-based data validation throughout

• Error Handling: Robust error handling with retry logic and circuit breakers

• TypeScript: Full TypeScript support with strict type checking

Quick Start

Pre-requisites

You could choose either to use google or vertex_ai. For simplicity, google provider is recommended.

Below are the environment variables you need to set based on your selected provider. (Note: It’s recommended to set the timeout configuration to more than 5 minutes for your MCP client).

(i) Using Google AI Studio Provider

Get your Google AI Studio's api key here

(ii) Using Vertex AI Provider

Refer to the guideline here on how to set this up.

Installation

Below are the installation guide for this MCP on different MCP clients, such as Claude Desktop, Claude Code, Cursor, Cline, etc.

Add to your Claude Desktop configuration:

(i) Using Google AI Studio Provider

(ii) Using Vertex AI Provider

(i) Using Google AI Studio Provider

(ii) Using Vertex AI Provider

Note: Increase the MCP startup timeout to 1 minutes and MCP tool execution timeout to about 5 minutes by updating ~\.claude\settings.json as follows:

Go to: Settings -> Cursor Settings -> MCP -> Add new global MCP server

Pasting the following configuration into your Cursor ~/.cursor/mcp.json file is the recommended approach. You may also install in a specific project by creating .cursor/mcp.json in your project folder. See Cursor MCP docs for more info.

(i) Using Google AI Studio Provider

(ii) Using Vertex AI Provider

Cline uses a JSON configuration file to manage MCP servers. To integrate the provided MCP server configuration:

1. Open Cline and click on the MCP Servers icon in the top navigation bar.

2. Select the Installed tab, then click Advanced MCP Settings.

3. In the cline_mcp_settings.json file, add the following configuration:

(i) Using Google AI Studio Provider

(ii) Using Vertex AI Provider

The server uses stdio transport and follows the standard MCP protocol. It can be integrated with any MCP-compatible client by running:

MCP Tools

The server provides four main MCP tools:

1) analyze_image

Analyzes an image using AI and returns a detailed description.

Parameters:

• imageSource (string): URL, base64 data, or file path to the image

• prompt (string): Question or instruction for the AI

• options (object, optional): Analysis options including temperature and max tokens

Examples:

1. Analyze image from URL:

2. Analyze local image file:

2) compare_images

Compares multiple images using AI and returns a detailed comparison analysis.

Parameters:

• imageSources (array): Array of image sources (URLs, base64 data, or file paths) - minimum 2, maximum 4 images

• prompt (string): Question or instruction for comparing the images

• options (object, optional): Analysis options including temperature and max tokens

Examples:

1. Compare images from URLs:

2. Compare mixed sources:

3) detect_objects_in_image

Detects objects in an image using AI vision models and generates annotated images with bounding boxes. Returns detected objects with coordinates and either saves the annotated image to a file or temporary directory.

Parameters:

• imageSource (string): URL, base64 data, or file path to the image

• prompt (string): Custom detection prompt describing what to detect or recognize in the image

• outputFilePath (string, optional): Explicit output path for the annotated image

Configuration: This function uses optimized default parameters for object detection and does not accept runtime options parameter. To customize the AI parameters (temperature, topP, topK, maxTokens), use environment variables:

File Handling Logic:

1. Explicit outputFilePath provided → Saves to the exact path specified

2. If not explicit outputFilePath → Automatically saves to temporary directory

Response Types:

• Returns file object when explicit outputFilePath is provided

• Returns tempFile object when explicit outputFilePath is not provided so the image file output is auto-saved to temporary folder

• Always includes detections array with detected objects and coordinates

• Includes summary with percentage-based coordinates for browser automation

Examples:

1. Basic object detection:

2. Save annotated image to specific path:

3. Custom detection prompt:

4) analyze_video

Analyzes a video using AI and returns a detailed description.

Parameters:

• videoSource (string): YouTube URL, GCS URI, or local file path to the video

• prompt (string): Question or instruction for the AI

• options (object, optional): Analysis options including temperature and max tokens

Supported video sources:

• YouTube URLs (e.g., https://www.youtube.com/watch?v=...)

• Local file paths (e.g., C:\Users\username\Downloads\video.mp4)

Examples:

1. Analyze video from YouTube URL:

2. Analyze local video file:

Note: Only YouTube URLs are supported for public video URLs. Other public video URLs are not currently supported.

Environment Configuration

For basic setup, you only need to configure the provider selection and required credentials:

Google AI Studio Provider (Recommended)

Vertex AI Provider (Production)

📖 Detailed Configuration Guide

For comprehensive environment variable documentation, including:

• Complete configuration reference (60+ environment variables)

• Function-specific optimization examples

• Advanced configuration patterns

• Troubleshooting guidance

👉 See Environment Variable Guide

Configuration Priority Overview

The server uses a hierarchical configuration system where more specific settings override general ones:

1. LLM-assigned values (runtime parameters in tool calls)

2. Function-specific variables (TEMPERATURE_FOR_ANALYZE_IMAGE, etc.)

3. Task-specific variables (TEMPERATURE_FOR_IMAGE, etc.)

4. Universal variables (TEMPERATURE, etc.)

5. System defaults

Basic Optimization:

Function-specific Optimization:

Model Selection:

Development

Prerequisites

• Node.js 18+

• npm or yarn

Setup

Scripts

• npm run build - Build the TypeScript project

• npm run dev - Start development server with watch mode

• npm run lint - Run ESLint

• npm run format - Format code with Prettier

• npm start - Start the built server

Architecture

The project follows a modular architecture:

Error Handling

The server includes comprehensive error handling:

• Validation Errors: Input validation using Zod schemas

• Network Errors: Automatic retries with exponential backoff

• Authentication Errors: Clear error messages for API key issues

• File Errors: Handling for file size limits and format restrictions

Contributing

1. Fork the repository

2. Create a feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes (git commit -m 'Add amazing feature')

4. Push to the branch (git push origin feature/amazing-feature)

5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Google for the Gemini and Vertex AI APIs

• The Model Context Protocol team for the MCP framework

• All contributors and users of this project