mcp-server-diff-python
Language:
Python
Stars:
5
Forks:
3
mcp-server-diff-python
An MCP server for obtaining text differences between two strings.
This server leverages Python's standard library difflib
to efficiently generate and provide differences between two texts in Unified diff format, making it ideal for text comparison and version control purposes.
Features
Tools
The server provides a single tool:
- get-unified-diff: Get differences between two texts in Unified diff format
- Arguments:
string_a
: Source text for comparison (required)string_b
: Target text to compare against (required)
- Return value: A string containing the differences in Unified diff format
- Arguments:
Usage
Claude Desktop
Using with Claude Desktop To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
"mcpServers": {
"mcp-server-diff-python": {
"command": "uvx",
"args": [
"mcp-server-diff-python"
]
}
}
or Add the following configuration:
git clone https://github.com/tatn/mcp-server-diff-python.git
cd mcp-server-diff-python
uv sync
uv build
"mcpServers": {
"mcp-server-diff-python": {
"command": "uv",
"args": [
"--directory",
"path\\to\\mcp-server-diff-python",
"run",
"mcp-server-diff-python"
]
}
}
Development
Debugging
You can start the MCP Inspector using npxwith the following commands:
npx @modelcontextprotocol/inspector uvx mcp-server-diff-python
npx @modelcontextprotocol/inspector uv --directory path\to\mcp-server-diff-python run mcp-server-diff-python
Publisher info
More MCP servers built with Python
Create, backtest, and execute trades directly in one chat box. The Composer MCP Server gives LLMs the power to backtest investment ideas and execute automated trading strategies. Trade across stocks, ETFs, and crypto directly in Claude.
An MCP to generate presentations with AI. Create and edit PowerPoint presentations with AI.
The PaddleOCR MCP server brings enterprise-grade OCR and document parsing capabilities to AI applications. Built on PaddleOCR — a proven solution with 50,000+ GitHub stars, deeply integrated by leading projects like MinerU, RAGFlow, and OmniParser— with targeted optimizations based on the MCP concept.