AI Vision MCP Server offers AI-powered visual analysis, screenshot capture, file operations, and report generation for MCP-compatible AI tools.
AI Vision MCP Server is a specialized service that leverages Model Context Protocol (MCP) to enrich AI applications, such as Claude Desktop, Continue, and Cursor, with sophisticated visual analysis capabilities. By integrating this server into the MCP ecosystem, these AI tools gain access to advanced functionalities like screenshot capture, file manipulation, report generation, and more. This document provides a detailed guide on how developers can implement and utilize this server to enhance their AI applications.
AI Vision MCP Server allows the capturing of screenshots from any website or webpage by providing a URL. The API screenshot_url
enables this feature, where users can specify whether to capture only the viewport (default) or the entire page (fullPage
parameter). Additionally, waiting for a specific CSS selector before taking the screenshot ensures that dynamic content is accurately captured.
AI Vision MCP Server utilizes advanced AI vision technologies to analyze UI elements, layouts, and content within screenshots. The analyze_screen
function leverages these analysis capabilities to provide detailed insights on the visual aspects of web applications, ensuring that developers can perform thorough UI/UX evaluations.
Managing files is a critical aspect of many applications. The server supports reading and modifying files with line-specific precision through the read_file
and modify_file
functions. This functionality allows for precise text manipulation within documents, making it easier to automate tasks or update content as needed.
Generating comprehensive UI/UX analysis reports is straightforward using the generate_report
function. Users can structure their findings in a detailed report format by specifying test URLs, application names, and custom observations. This helps in documenting critical aspects of web applications during testing phases.
Maintaining context across multiple analysis steps is crucial for efficient development cycles. The AI Vision MCP Server ensures seamless debugging sessions by keeping track of previous analyses and results. This feature enhances the usability and productivity of developers working with complex applications.
The AI Vision MCP Server is built on a robust architecture that seamlessly integrates into the Model Context Protocol (MCP) framework. The server is designed to be lightweight yet versatile, enabling it to connect with diverse AI clients through standardized communication protocols. By adhering to MCP standards, this server ensures compatibility and interoperability across different environments.
The following Mermaid diagram illustrates the flow of data between an MVP client, the MCP protocol, and the AI Vision MCP Server:
graph TD
A[AI Application] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server]
C --> D[Data Source/Tool]
style A fill:#e1f5fe
style C fill:#f3e5f5
style D fill:#e8f5e8
This diagram shows how the MVP client communicates with the MCP Protocol, which in turn interacts with the AI Vision MCP Server and connected data sources or tools. This streamlined process ensures efficient data exchange and robust integration.
The server's internal architecture is designed to support various data formats and storage mechanisms. By leveraging Playwright for browser automation and Gemini API key for visual analysis, the AI Vision MCP Server can handle complex data processing tasks seamlessly. The following Mermaid diagram provides a detailed overview of the data flow within the server:
graph TD
A[Input Data] --> B[MCP Protocol]
B --> C[Parser Module]
C --> D[MCP Client Communication Layer]
D --> E[Data Storage & Retrieval Layer]
style A fill:#f5ede6
style C fill:#c6e5ff
style D fill:#d9efdb
To get started with the AI Vision MCP Server, follow these steps:
Ensure that you have Node.js version 14 or higher installed on your system. Additionally, install Playwright for supporting browser automation tasks.
# Clone the repository
git clone https://github.com/samihalawa/mcp-server-ai-vision.git
cd mcp-server-ai-vision
# Install dependencies
npm install
# Build the server
npm run build
To start the server, execute:
npm start
Developers can use this server to capture and analyze screenshots of web applications during testing phases. By integrating screenshot_url
and analyze_screen
, developers can quickly identify issues and ensure that their applications meet design specifications.
AI Vision MCP Server supports automated UI testing by capturing and analyzing screenshots at various stages of application development. The generate_report
function can be used to document findings, making it easier for teams to track progress and address any inconsistencies identified during these tests.
The following table outlines the MCP client compatibility matrix for AI Vision MCP Server:
MCP Client | Resources | Tools | Prompts | Status |
---|---|---|---|---|
Claude Desktop | ✅ | ✅ | ✅ | Full Support |
Continue | ✅ | ✅ | ✅ | Full Support |
Cursor | ❌ | ✅ | ❌ | Tools Only |
The AI Vision MCP Server is designed to be compatible with a wide range of devices and environments. It leverages modern web technologies and APIs, ensuring that it performs efficiently across different platforms.
Customization of the server is essential for advanced use cases. Users can modify various settings in the MCP configuration to tailor the server's behavior according to their needs.
{
"mcpServers": {
"ai-vision": {
"command": "/path/to/node",
"args": ["/path/to/mcp-server-ai-vision/build/index.js"],
"enabled": true,
"port": 3005,
"environment": {
"NODE_PATH": "/path/to/node_modules",
"PATH": "/usr/local/bin:/usr/bin:/bin",
"GEMINI_API_KEY": "your-gemini-api-key"
}
}
}
}
How do I integrate AI Vision MCP Server with other MCP clients?
Can I customize the server’s behavior for specific use cases?
How does AI Vision MCP Server ensure data security during transmissions?
What APIs are available for developers to leverage these advanced capabilities?
screenshot_url
, analyze_screen
, read_file
, modify_file
, and generate_report
functions provided by AI Vision MCP Server.Is there any documentation or support available if I encounter issues during integration?
Contributions to the AI Vision MCP Server are welcome! Developers can participate by submitting bug reports, feature requests, and pull requests. The process for contributing includes setting up a development environment, running tests, and submitting pull requests through GitHub.
npm install
.The AI Vision MCP Server is part of a larger ecosystem designed to facilitate seamless integration between diverse AI applications and their respective data sources or tools. For more information, explore the provided documentation, participate in community forums, and consult relevant resources.
By following this comprehensive documentation, developers can effectively integrate the AI Vision MCP Server into their projects, enhancing functionality and performance for a wide range of applications.
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