@iflow-mcp/copy-paste-mcp

Copy-Paste MCP

A Model Context Protocol (MCP) server that provides a tool for extracting specific lines from text content.

Features

• Simple tool to extract specific line ranges from any text content
• Preserves exact content formatting and newlines
• No modification of content - pure extraction
• MCP server implementation for easy integration with AI tools

MCP Server

This package includes an MCP server that exposes the line extraction functionality as a tool that can be used by any MCP client like Claude Desktop, VS Code, or other MCP-compatible applications.

Available Tools

• extract-lines - Extract a specific range of lines from text content

Installation

• Clone the repository
• Install dependencies:

npm install

Usage

Starting the MCP Server

npm start

This will start the MCP server using the stdio transport, which can be connected to by MCP clients.

Connecting to the Server

In an MCP-compatible client (like Claude Desktop), you can add this server with:

• Name: Copy-Paste
• Command: node /path/to/copy-paste-mcp/dist/index.js
• Transport: stdio

Using the Tool

Once connected, you can use the tool through your MCP client's interface:

Extract Lines

Tool: extract-lines
Parameters:
  - text: "Your multi-line text content goes here\nSecond line\nThird line"
  - start_line: 1
  - end_line: 2

This will return the first two lines of the provided text content.

Examples

Extract lines 10-20 from a large text block:

Tool: extract-lines
Parameters:
  - text: [your text content with many lines]
  - start_line: 10
  - end_line: 20

Extract just a single line (line 5):

Tool: extract-lines
Parameters:
  - text: [your text content]
  - start_line: 5
  - end_line: 5

Why use this?

This tool is useful when you need to:

• Extract specific sections from large text blocks
• Copy exact line ranges from code or documentation
• Ensure precise content extraction without any alterations
• Integrate line extraction capabilities into AI workflows via MCP
• Work with specific portions of large documents