Macos Screenshot
An MCP server that provides screen capture and OCR text recognition functions
rating : 2.5 points
downloads : 10.0K
What is the MCP Screenshot Server?
The MCP Screenshot Server is a tool that can capture screenshots and use OCR technology to recognize text. It supports multiple languages (such as Japanese and English) and can generate different output formats to meet various needs.How to use the MCP Screenshot Server?
You can make the server capture the screen and recognize text through simple instructions. For example, you can request it to capture the left half of the screen and extract the text in it.Applicable Scenarios
Suitable for scenarios where you need to quickly extract text from the screen, such as learning, meeting notes, or document processing.Main Features
Screenshot Function
Supports left half-screen, right half-screen, and full-screen screenshots.
OCR Text Recognition
Supports multiple languages, including but not limited to Japanese and English.
Multi-format Output
Supports JSON, Markdown, vertical, and horizontal text output.
Advantages
High-precision OCR text recognition, especially good at Japanese.
Supports multiple output formats for easy subsequent processing.
Easy to use and can start working without complex configuration.
Limitations
May not be able to fully accurately recognize some special fonts.
Requires a network environment to support the operation of the OCR engine.
How to Use
Install the MCP Screenshot Server
Easily install the MCP Screenshot Server via npm.
Configure the Claude Desktop Client
Add relevant settings to the configuration file of the Claude desktop client.
Usage Examples
Example 1: Study Notes
During the learning process, capture important content on the classroom PPT and automatically extract the text.
Example 2: Meeting Minutes
During a meeting, capture the content displayed on the projector and automatically generate records.
Frequently Asked Questions
How to install the MCP Screenshot Server?
Does it support other languages?
How to ensure the accuracy of OCR recognition?
Related Resources
GitHub Repository
View the source code and project documentation.
Documentation Page
Learn more about the MCP Screenshot Server.

Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
14.8K
4.5 points

Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
24.8K
5 points

Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
15.6K
4.3 points

Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
44.5K
4.3 points

Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
20.3K
5 points

Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
45.6K
4.5 points

Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
15.0K
4.5 points

Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
63.1K
4.7 points





