Copy-Paste MCP
The Copy-Paste MCP server enables precise text extraction while preserving formatting. With this server, you can:
Extract specific line ranges from text content (inclusive start and end lines)
Preserve original formatting, spacing, and newlines during extraction
Handle both single-line and multi-line extractions without modifying content
Work effectively with large text blocks or documents
Select and process specific sections from larger content
Integrate with MCP-compatible tools like Claude Desktop, VS Code, and other AI applications
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Copy-Paste MCPextract lines 5-10 from this code snippet"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Related MCP server: MCP PDF Reader
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 installUsage
Starting the MCP Server
npm startThis 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.jsTransport: 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: 2This 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: 20Extract just a single line (line 5):
Tool: extract-lines
Parameters:
- text: [your text content]
- start_line: 5
- end_line: 5Why 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
Available Tools
1 toolextract-linesB
Extract a specific range of lines from text content
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The full text content to extract lines from | |
| start_line | Yes | Starting line number (inclusive) | |
| end_line | Yes | Ending line number (inclusive) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the extraction action but doesn't describe what happens with invalid inputs (e.g., start_line > end_line), whether the operation is read-only or has side effects, error handling, or output format. This leaves significant behavioral gaps for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's appropriately sized for this simple tool and front-loads the core functionality clearly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple text processing tool with no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It covers the basic purpose but lacks behavioral details and usage context that would help an agent use it correctly in practice.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all three parameters well-documented in the schema. The description adds no additional parameter semantics beyond implying a range operation. This meets the baseline of 3 when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('extract') and resource ('lines from text content'), and specifies the scope ('a specific range'). It doesn't need sibling differentiation since there are no sibling tools. However, it could be slightly more specific about what 'extract' means operationally.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or constraints. It simply states what the tool does without context about appropriate use cases or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
- First observed
extract-lines
TDQS
With only one tool, there is no possibility of confusion or overlap with other tools, making disambiguation perfect.
The single tool uses a clear verb_noun pattern (extract-lines), and with no other tools to compare, consistency is inherently perfect.
One tool is too few for a server named 'Copy-Paste MCP', which suggests broader copy-paste functionality, making the scope feel incomplete and thin.
The tool only extracts lines from text, lacking obvious operations like copying full text, pasting, or handling other copy-paste tasks, indicating significant gaps for the domain.
Maintenance
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