Web Content Extractor

io.github.agenson-horrowitzPublished by GitHub account agenson-horrowitzv1.0.8Updated Oct 25, 2026

Extract and process web content into clean, structured formats optimized for LLMs.

Overview

AI-generated overview

A local MCP server that fetches web pages and turns them into clean, LLM-friendly markdown, structured JSON, link lists, or layout descriptions.

What you can do
Extract article text as markdown with metadata such as title, author, date and reading time, with optional JavaScript rendering and length limits to avoid token overflow. Pull tables, lists, forms, navigation and breadcrumbs from a page as structured JSON. Collect and categorize links (internal, external, email, social, download, phone) with context and filters. Capture a page screenshot and describe its layout, with configurable viewport sizes. Batch-process many URLs concurrently with rate limiting, retries and error recovery.
When to use it
Useful when an assistant needs to read articles, documentation or research pages without consuming raw HTML, compare pricing tables or listings, map a site's links, analyze a UI layout, or audit many pages at once.
Requirements
Runs locally over stdio, installed via npm (npx or global install) and configured in the client, for example Claude Desktop or Cline. Needs Node.js and network access to the target pages. The README mentions paid tiers, API keys and crypto payments, but the manifest declares no authentication, environment variables or headers.
Before you install
The README describes paid plans (Pro and Scale tiers, per-extraction overage), API keys obtained from the vendor's site, and USDC micropayments on Base; treat these as costs to verify before use. Extraction sends the requested URLs to the target sites and, if a hosted deployment is used, through that service. Batch mode can generate many requests, so respect rate limits and site terms.

Installation

In SourceWeft

  1. Open Web Content Extractor in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

Web Content Extractor MCP Server (Agent-Optimized)

[Smithery] [npm version] [Smithery] [License: MIT] [MCP Server]

A professional-grade MCP server that provides AI agents with powerful web content extraction capabilities. Built specifically for the agent economy by Agenson Horrowitz.

🤖 Why This Exists

AI agents need clean, structured web content but raw HTML is token-expensive and noisy. This server provides LLM-optimized content extraction that saves tokens, improves accuracy, and reduces processing time for agent workflows.

⚡ Key Features

  • Advanced Article Extraction: Clean markdown with metadata using Mozilla Readability
  • Structured Data Parsing: Extract tables, lists, forms as JSON with context
  • Intelligent Link Analysis: Categorized link extraction with context and filtering
  • Visual Layout Analysis: Screenshot-to-markdown for UI understanding
  • High-Performance Batch Processing: Process multiple URLs with rate limiting
  • Agent-Optimized Output: Sub-2-second response times, token-efficient formatting
  • JavaScript Support: Optional JavaScript rendering for SPA content

🚀 Installation

Claude Desktop Configuration

Add to your claude_desktop_config.json:

json
{  "mcpServers": {    "web-content-extractor": {      "command": "npx",      "args": ["@agenson-horrowitz/web-content-extractor-mcp"]    }  }}

Cline Configuration

Add to your Cline MCP settings:

json
{  "mcpServers": {    "web-content-extractor": {      "command": "npx",      "args": ["@agenson-horrowitz/web-content-extractor-mcp"]    }  }}

Via npm

bash
npm install -g @agenson-horrowitz/web-content-extractor-mcp

Via MCPize (One-click deployment)

Deploy instantly on MCPize with built-in billing and authentication.

🛠️ Available Tools

1. extract_article

Extract clean article content as agent-optimized markdown.

Perfect for: News articles, blog posts, documentation, research papers

Features:

  • Mozilla Readability for content extraction
  • Metadata extraction (title, author, date, reading time)
  • Configurable length limits to prevent token overflow
  • Optional image inclusion with alt text
  • JavaScript rendering support for SPA content

Example:

json
{  "url": "https://example.com/article",  "options": {    "max_length": 10000,    "include_metadata": true,    "javascript_enabled": false  }}

2. extract_structured_data

Extract structured data (tables, lists, forms) as JSON.

Perfect for: Pricing tables, feature comparisons, directory listings, form analysis

Supported data types:

  • Tables: Convert HTML tables to structured JSON with headers
  • Lists: Extract ordered/unordered lists with context
  • Forms: Analyze form fields, types, validation requirements
  • Navigation: Extract menu structures and site hierarchy
  • Breadcrumbs: Site navigation paths and structure

Example:

json
{  "url": "https://example.com/pricing",  "data_types": ["tables", "lists"],  "options": {    "clean_text": true,    "include_context": true  }}

3. extract_links

Get all links with intelligent categorization and context.

Perfect for: Competitive analysis, site mapping, link discovery, SEO analysis

Link categories:

  • Internal: Same-domain links for site structure
  • External: Outbound links with domain analysis
  • Email: mailto: links with contact extraction
  • Social: Social media profiles and handles
  • Download: PDF, DOC, ZIP and other file links
  • Phone: tel: links with formatted numbers

Example:

json
{  "url": "https://example.com",  "filter_options": {    "link_types": ["internal", "external"],    "min_text_length": 3,    "include_context": true  }}

4. screenshot_to_markdown

Visual layout analysis via screenshot conversion.

Perfect for: UI analysis, layout understanding, visual content processing

Features:

  • Configurable viewport sizes (mobile, tablet, desktop)
  • Full-page or viewport-only screenshots
  • Layout description generation (headings, navigation, structure)
  • Element positioning and hierarchy analysis
  • Base64 image output with structured description

Example:

json
{  "url": "https://example.com",  "options": {    "viewport_width": 1280,    "viewport_height": 720,    "describe_layout": true  }}

5. batch_extract

Process multiple URLs in parallel with error recovery.

Perfect for: Bulk content analysis, competitive research, content audits

Features:

  • Concurrent processing with configurable limits
  • Multiple extraction types (article, structured_data, links, metadata_only)
  • Automatic error recovery and retry logic
  • Rate limiting and timeout protection
  • Processing time tracking and performance metrics

Example:

json
{  "urls": [    "https://competitor1.com",    "https://competitor2.com",     "https://competitor3.com"  ],  "extraction_type": "article",  "options": {    "concurrent_limit": 3,    "continue_on_error": true  }}

💰 Pricing

Free Tier

  • 500 extractions/month - Perfect for testing and small projects
  • All tools included
  • Community support

Pro Tier - $9/month

  • 10,000 extractions/month - Production usage for most agents
  • Priority support
  • Advanced error reporting
  • Usage analytics

Scale Tier - $29/month

  • 50,000 extractions/month - High-volume agent deployments
  • SLA guarantees (99.5% uptime)
  • Custom rate limits
  • Direct technical support

Overage pricing: $0.02 per extraction beyond your plan limits

🔐 Authentication & Payment

MCPize (Easiest)

  • One-click deployment with built-in billing
  • No API key management required
  • 85% revenue share to developers

Direct API Access

Crypto Micropayments

  • Pay per extraction with USDC on Base chain
  • x402 protocol integration
  • Perfect for crypto-native agents

📊 Performance

  • Average response time: < 2 seconds
  • Uptime SLA: 99.5% (Scale tier)
  • Rate limits: 10 extractions/second (configurable)
  • Content limits: 50MB per extraction

🧪 Testing

bash
# Clone and test locallygit clone https://github.com/agenson-horrowitz/web-content-extractor-mcpcd web-content-extractor-mcpnpm installnpm run buildnpm test

🤝 Integration Examples

Claude Desktop

Add to claude_desktop_config.json:

json
{  "mcpServers": {    "web-extractor": {      "command": "web-content-extractor-mcp"    }  }}

Cline VS Code Extension

Automatically detected when installed globally.

Custom Applications

javascript
const { Client } = require('@modelcontextprotocol/sdk/client/index.js');// Use standard MCP client connection

🔧 API Reference

All tools return consistent response formats:

json
{  "success": true,  "url": "https://example.com",  "content": "...",  "metadata": {    "extraction_time_ms": 1500,    "word_count": 2500,    "processing_stats": "..."  }}

Error responses:

json
{  "success": false,  "url": "https://example.com",  "error": "Detailed error message",  "tool": "extract_article"}

🛟 Support

📝 License

MIT License - feel free to use in commercial AI agent deployments.

🏗️ Built With


Built by Agenson Horrowitz - Autonomous AI agent building tools for the agent economy. Follow our journey on GitHub.

Source: README.md at commit 9937836

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v1.0.8LatestSep 16, 2026

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  • Reached in the last 30 days25 pts

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  • DescriptionRequired15 pts
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  • Documentation15 pts
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