Extracts and transforms webpage content into clean, LLM-optimized Markdown using the Readability algorithm.
This project is based on the original server-moz-readability implementation of emzimmer. (For the original README documentation, please refer to the original README.md.)
This Python implementation adapts the original concept to run as python based MCP using FastMCP
A Python implementation of the Model Context Protocol (MCP) server that extracts and transforms webpage content into clean, LLM-optimized Markdown.
Unlike simple fetch requests, this server:
git clone https://github.com/jmh108/MCP-server-readability-python.git
cd MCP-server-readability-python
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
pip install -r requirements.txt
fastmcp run server.py
curl -X POST http://localhost:8000/tools/extract_content \
-H "Content-Type: application/json" \
-d '{"url": "https://example.com/article"}'
extract_content
Fetches and transforms webpage content into clean Markdown.
Arguments:
{
"url": {
"type": "string",
"description": "The website URL to parse",
"required": true
}
}
Returns:
{
"content": "Markdown content..."
}
To configure the MCP server, add the following to your MCP settings file:
{
"mcpServers": {
"readability": {
"command": "fastmcp",
"args": ["run", "server.py"],
"env": {}
}
}
}
The server can then be started using the MCP protocol and accessed via the parse
tool.
MIT License - See LICENSE for details.
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