Readability (Fetch & Parse)
БесплатноНе проверенExtracts and transforms webpage content into clean, LLM-optimized Markdown by removing non-essential elements while preserving key metadata.
Описание
Extracts and transforms webpage content into clean, LLM-optimized Markdown by removing non-essential elements while preserving key metadata.
README
Credits/Reference
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
Mozilla Readability Parser MCP Server
A Python implementation of the Model Context Protocol (MCP) server that extracts and transforms webpage content into clean, LLM-optimized Markdown.
Table of Contents
Features
- Removes ads, navigation, footers and other non-essential content
- Converts clean HTML into well-formatted Markdown
- Handles errors gracefully
- Optimized for LLM processing
- Lightweight and fast
Why Not Just Fetch?
Unlike simple fetch requests, this server:
- Extracts only relevant content using Readability algorithm
- Eliminates noise like ads, popups, and navigation menus
- Reduces token usage by removing unnecessary HTML/CSS
- Provides consistent Markdown formatting for better LLM processing
- Handles complex web pages with dynamic content
Installation
- Clone the repository:
git clone https://github.com/jmh108/MCP-server-readability-python.git
cd MCP-server-readability-python
- Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
- Install dependencies:
pip install -r requirements.txt
Quick Start
- Start the server:
fastmcp run server.py
- Example request:
curl -X POST http://localhost:8000/tools/extract_content \
-H "Content-Type: application/json" \
-d '{"url": "https://example.com/article"}'
Tool Reference
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..."
}
MCP Server Configuration
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.
Dependencies
- readability-lxml - Content extraction
- html2text - HTML to Markdown conversion
- beautifulsoup4 - DOM parsing
- requests - HTTP requests
License
MIT License - See LICENSE for details.
Установка Readability (Fetch & Parse)
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/jmh108/mcp-server-readability-pythonFAQ
Readability (Fetch & Parse) MCP бесплатный?
Да, Readability (Fetch & Parse) MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Readability (Fetch & Parse)?
Нет, Readability (Fetch & Parse) работает без API-ключей и переменных окружения.
Readability (Fetch & Parse) — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить Readability (Fetch & Parse) в Claude Desktop, Claude Code или Cursor?
Открой Readability (Fetch & Parse) на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
Похожие MCP
Fetch
Web content fetching and conversion for efficient LLM usage.
AWS KB Retrieval
Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.
автор: modelcontextprotocolSpring AI MCP Server
Provides auto-configuration for setting up an MCP server in Spring Boot applications.
llm-analysis-assistant
A very streamlined mcp client that supports calling and monitoring stdio/sse/streamableHttp, and can also view request responses through the /logs page. It also
автор: xuzexin-hzCompare Readability (Fetch & Parse) with
Не уверен что выбрать?
Найди свой стек за 60 секунд
Автор?
Embed-бейдж для README
Похожее
Все в категории ai
