Single-File MCP Server

A powerful Model Context Protocol (MCP) server that provides intelligent web content extraction using single-file and trafilatura. Perfect for AI agents that need to access and analyze web content from JavaScript-heavy sites.

GitHub Repository: https://github.com/kwinsch/singlefile-mcp

Features

🌐 Universal Web Content Access

• JavaScript Support: Handles modern SPA/React/Vue apps that require browser rendering
• Clean Content Extraction: Uses Mozilla's Readability algorithm via trafilatura
• Rich Metadata: Extracts title, author, date, description, and more
• Multiple Output Formats: Raw HTML or clean markdown-like content

📄 Smart Pagination & Token Management

• Flexible Pagination: Offset/limit system like file reading tools
• Token Limits: Configurable max tokens (up to 25,000)
• Smart Truncation: Summary mode shows beginning + end, truncate mode cuts cleanly
• Navigation Hints: Clear guidance on how to continue reading large documents

⚡ Performance & Control

• Selective Loading: Block images/scripts for faster processing
• Content Compression: Optional HTML compression
• Timeout Protection: Configurable timeouts prevent hanging
• Error Handling: Graceful degradation when extraction fails

Installation

Prerequisites

• Python 3.8+
• single-file CLI - Web page capture tool
• Node.js 16+ (for single-file)
• A supported browser (Chromium, Chrome, Edge, Firefox, etc.)

Install single-file CLI

The single-file CLI is essential for this MCP server to work. It uses a real browser engine to accurately capture JavaScript-rendered content.

npm install -g single-file-cli

Usage with Claude Code

Quick Install (from PyPI)

claude mcp add singlefile-mcp -s user -- uvx singlefile-mcp

This will automatically install and run the package from PyPI, similar to how Brave Search works!

Development Install (from local directory)

claude mcp add singlefile-mcp -s user -- uvx --from /path/to/single-file_mcp singlefile-mcp

Remove old server (if upgrading)

claude mcp remove single-file-fetcher --scope user

Optional: Add Brave Search MCP

claude mcp add brave-search -s user -- env BRAVE_API_KEY=YOUR_KEY npx -y @modelcontextprotocol/server-brave-search

API Reference

fetch_webpage

Fetch and process web content with intelligent extraction.

Parameters

Parameter	Type	Default	Description
url	string	required	URL of the webpage to fetch
output_content	boolean	true	Whether to return content in response
extract_content	boolean	false	Extract clean text content (recommended)
include_metadata	boolean	true	Include page metadata (title, author, etc.)
block_images	boolean	false	Block image downloads for faster processing
block_scripts	boolean	true	Block JavaScript execution
compress_html	boolean	true	Compress HTML output
max_tokens	number	20000	Maximum tokens in response (max: 25000)
truncate_method	string	"truncate"	How to handle large content: "truncate" or "summary"
offset	number	0	Character offset to start reading from
limit	number	null	Maximum characters to return

Examples

Basic content extraction:

fetch_webpage(
    url="https://example.com/article",
    extract_content=True,
    include_metadata=True
)

Paginated reading of large documents:

# Get overview
fetch_webpage(
    url="https://docs.example.com/guide",
    extract_content=True,
    limit=5000
)

# Continue reading from offset
fetch_webpage(
    url="https://docs.example.com/guide", 
    extract_content=True,
    offset=5000,
    limit=5000
)

Raw HTML for complex parsing:

fetch_webpage(
    url="https://app.example.com/dashboard",
    extract_content=False,
    block_scripts=False,
    max_tokens=15000
)

Practical Example: Research Workflow

Here's a real-world example combining Brave Search and Single-File MCP:

Step 1: Search for information

# Using Brave Search MCP
brave_web_search(
    query="artificial intelligence history timeline",
    count=5
)

Step 2: Fetch and analyze Wikipedia article

# Using Single-File MCP to extract content
fetch_webpage(
    url="https://en.wikipedia.org/wiki/History_of_artificial_intelligence",
    extract_content=True,
    include_metadata=True,
    limit=5000  # Get first 5000 chars
)

Result:

Successfully fetched webpage: https://en.wikipedia.org/wiki/History_of_artificial_intelligence

## Metadata
**Title:** History of artificial intelligence - Wikipedia
**Description:** The history of artificial intelligence (AI) began in antiquity...
**Site:** wikipedia.org

## Extracted Content (chars 0-5000 of 45000)
*Note: More content available. Use offset=5000 to continue.*

# History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, 
stories and rumors of artificial beings endowed with intelligence...

[Clean, readable article content follows...]

Step 3: Continue reading with pagination

# Get next section
fetch_webpage(
    url="https://en.wikipedia.org/wiki/History_of_artificial_intelligence",
    extract_content=True,
    offset=5000,
    limit=5000
)

This workflow enables AI agents to:

1. Search for current information beyond their training data
2. Extract clean, structured content from any webpage
3. Process JavaScript-heavy sites that other tools can't handle
4. Paginate through long documents intelligently

Output Format

With Content Extraction

Successfully fetched webpage: https://example.com

## Metadata
**Title:** Example Article
**Author:** John Doe
**Date:** 2024-01-15
**Description:** An informative article about...
**Site:** example.com

## Extracted Content (chars 0-5000 of 12000)
*Note: More content available. Use offset=5000 to continue.*

# Article Title

This is the clean, readable content extracted from the webpage...

Pagination Info

When using offset/limit, responses include:

• Current position: chars 1000-6000 of 12000
• Navigation hint: Use offset=6000 to continue
• Total size information

Use Cases

📚 Documentation Analysis

Perfect for reading large technical docs, API references, and guides that span multiple pages.

📰 News & Article Processing

Extract clean article content from news sites, blogs, and publications for analysis.

🔍 Research & Data Gathering

Gather structured data from websites, including metadata and clean text content.

🤖 AI Agent Integration

Enable AI agents to browse and understand web content, even from JavaScript-heavy applications.

⚖️ Legal Document Processing

Handle complex legal documents and government sites that require JavaScript rendering.

Technical Details

Content Extraction Pipeline

1. single-file: Renders JavaScript and saves complete webpage
2. trafilatura: Extracts main content using Mozilla Readability algorithm
3. Pagination: Applies offset/limit for manageable chunks
4. Token Management: Ensures responses fit within LLM context limits

Browser Engine

Uses a browser via single-file for full JavaScript support:

• Works with any supported browser installed on your system
• Waits for network idle before capture
• Removes hidden elements and unused styles
• Handles dynamic content loading

Metadata Extraction

Automatically extracts:

• Page title and description
• Author and publication date
• Site name and language
• Categories and tags (when available)

Error Handling

• Network Issues: Graceful timeout with informative errors
• JavaScript Errors: Continues processing even if some scripts fail
• Large Content: Automatic truncation with clear indicators
• Invalid URLs: Clear validation error messages

Development Setup

1. Clone the repository:

git clone https://github.com/kwinsch/singlefile-mcp.git
cd singlefile-mcp

2. Install dependencies:

pip install -r requirements.txt

3. Install in development mode:

pip install -e .

4. Test locally with Claude Code:

claude mcp add singlefile-mcp -s user -- uvx --from . singlefile-mcp

License

MIT License - see LICENSE file for details.

Dependencies

• single-file - Core web page capture tool that handles JavaScript rendering
• trafilatura - Content extraction using Mozilla's Readability algorithm
• mcp - Model Context Protocol for AI integration

Acknowledgments

• single-file by Gildas Lormeau - Excellent web page capture tool
• trafilatura - Robust content extraction library
• Model Context Protocol - Standardized AI integration protocol