search-web

JinaAI

spences10

by spences10

JinaAI offers advanced web scraping tools and software for efficient extraction and parsing of web page content and data

Extracts and processes web content for efficient parsing and analysis of online information

github stars

31

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Repository deprecated - use mcp-omnisearch insteadRequires Jina.ai API key

best for

  • / Analyzing web documentation and articles
  • / Processing online content for AI workflows
  • / Converting web pages for LLM analysis

capabilities

  • / Extract text content from any URL
  • / Convert web pages to LLM-friendly format
  • / Preserve document structure during extraction
  • / Process various content types including documentation

what it does

Extracts and converts web content from URLs into clean, structured text that's optimized for LLM processing using Jina.ai's Reader API.

about

JinaAI is a community-built MCP server published by spences10 that provides AI assistants with tools and capabilities via the Model Context Protocol. JinaAI offers advanced web scraping tools and software for efficient extraction and parsing of web page content and data It is categorized under search web.

how to install

You can install JinaAI in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

JinaAI is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

mcp-jinaai-reader


⚠️ Notice

This repository is no longer maintained.

The functionality of this tool is now available in mcp-omnisearch, which combines multiple MCP tools in one unified package.

Please use mcp-omnisearch instead.


A Model Context Protocol (MCP) server for integrating Jina.ai's Reader API with LLMs. This server provides efficient and comprehensive web content extraction capabilities, optimized for documentation and web content analysis.

<a href="https://glama.ai/mcp/servers/a75afsx9cx"> <img width="380" height="200" src="https://glama.ai/mcp/servers/a75afsx9cx/badge" /> </a>

Features

  • 📚 Advanced web content extraction through Jina.ai Reader API
  • 🚀 Fast and efficient content retrieval
  • 📄 Complete text extraction with preserved structure
  • 🔄 Clean format optimized for LLMs
  • 🌐 Support for various content types including documentation
  • 🏗️ Built on the Model Context Protocol

Configuration

This server requires configuration through your MCP client. Here are examples for different environments:

Cline Configuration

Add this to your Cline MCP settings:

{
	"mcpServers": {
		"jinaai-reader": {
			"command": "node",
			"args": ["-y", "mcp-jinaai-reader"],
			"env": {
				"JINAAI_API_KEY": "your-jinaai-api-key"
			}
		}
	}
}

Claude Desktop with WSL Configuration

For WSL environments, add this to your Claude Desktop configuration:

{
	"mcpServers": {
		"jinaai-reader": {
			"command": "wsl.exe",
			"args": [
				"bash",
				"-c",
				"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-reader"
			]
		}
	}
}

Environment Variables

The server requires the following environment variable:

  • JINAAI_API_KEY: Your Jina.ai API key (required)

API

The server implements a single MCP tool with configurable parameters:

read_url

Convert any URL to LLM-friendly text using Jina.ai Reader.

Parameters:

  • url (string, required): URL to process
  • no_cache (boolean, optional): Bypass cache for fresh results. Defaults to false
  • format (string, optional): Response format ("json" or "stream"). Defaults to "json"
  • timeout (number, optional): Maximum time in seconds to wait for webpage load
  • target_selector (string, optional): CSS selector to focus on specific elements
  • wait_for_selector (string, optional): CSS selector to wait for specific elements
  • remove_selector (string, optional): CSS selector to exclude specific elements
  • with_links_summary (boolean, optional): Gather all links at the end of response
  • with_images_summary (boolean, optional): Gather all images at the end of response
  • with_generated_alt (boolean, optional): Add alt text to images lacking captions
  • with_iframe (boolean, optional): Include iframe content in response

Development

Setup

  1. Clone the repository
  2. Install dependencies:
npm install
  1. Build the project:
npm run build
  1. Run in development mode:
npm run dev

Publishing

  1. Update version in package.json
  2. Build the project:
npm run build
  1. Publish to npm:
npm publish

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see the LICENSE file for details.

Acknowledgments

FAQ

What is the JinaAI MCP server?
JinaAI is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for JinaAI?
This profile displays 51 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Web Research & Information Gathering

Fetch and extract information from websites automatically

Example

Research competitor pricing, scrape product reviews, monitor news mentions

Automate 5-10 hours/week of manual web research

Content Monitoring & Alerts

Track website changes, new content, price updates

Example

Monitor competitor blog for new posts, track stock availability, watch for pricing changes

Stay informed without manual checking, never miss important updates

Data Extraction & Aggregation

Extract structured data from multiple websites

Example

Compile product listings from 10 e-commerce sites, aggregate job postings, collect real estate data

Build datasets 100x faster than manual copying

API-less Integration

Interact with services that don't offer APIs

Example

Check form submissions, validate website functionality, test user flows

Automate interactions with any website, even without API

Implementation Guide

Prerequisites

  • Claude Desktop or Cursor with MCP support
  • Understanding of web scraping ethics and robots.txt
  • Rate limiting awareness to avoid overwhelming target sites
  • Knowledge of legal restrictions on data collection

Time Estimate

20-40 minutes including configuration and testing

Installation Steps

  1. 1.Install web automation MCP server via npm or pip
  2. 2.Configure allowed domains and rate limits in MCP config
  3. 3.Test with simple fetch: 'Get content from example.com'
  4. 4.Progress to extraction: 'Extract all product prices from this page'
  5. 5.Set up monitoring: 'Check this URL daily for changes'
  6. 6.Parse structured data: 'Create CSV from this table'
  7. 7.Respect robots.txt and rate limits always

Troubleshooting

  • 403 Forbidden: Website blocks bots—respect their wishes, use official API instead
  • Rate limit errors: Slow down requests, add delays between fetches
  • Stale data: Target site changed HTML structure—update selectors
  • Timeout errors: Site is slow or blocking—increase timeout, try different user agent
  • JavaScript-rendered content: Use headless browser MCP servers for dynamic sites

Best Practices

✓ Do

  • +Check robots.txt and respect crawl rules
  • +Rate limit requests: 1-2 requests/second maximum
  • +Use official APIs when available instead of scraping
  • +Identify your bot with descriptive user agent
  • +Cache results to minimize repeated requests
  • +Handle errors gracefully with retries and fallbacks
  • +Validate extracted data for accuracy

✗ Don't

  • Don't scrape sites that explicitly forbid it (robots.txt, ToS)
  • Don't overwhelm servers with rapid requests—use rate limiting
  • Don't scrape personal data without consent and legal basis
  • Don't ignore copyright on extracted content
  • Don't assume HTML structure is stable—handle changes
  • Don't use scraped data for commercial purposes without permission

💡 Pro Tips

  • Use CSS selectors or XPath for robust data extraction
  • Set up monitoring alerts for extraction failures (structure changed)
  • Implement exponential backoff for retries on failures
  • Store raw HTML for reprocessing if extraction logic changes
  • Combine with data analysis tools for insights from extracted data
  • Consider using official APIs or RSS feeds as more stable alternatives

Technical Details

Architecture

MCP server handles HTTP requests, HTML parsing, JavaScript rendering (if headless browser), and returns structured data to Claude.

Protocols

  • HTTP/HTTPS
  • WebSocket (for real-time sites)
  • Puppeteer/Playwright (for JavaScript sites)

Compatibility

  • Static HTML sites
  • JavaScript-rendered SPAs (with headless browser)
  • REST APIs
  • GraphQL endpoints

When to Use This

✓ Use When

Use for research automation, content monitoring, data aggregation from multiple sources, and when official APIs don't exist. Best for read-only information gathering.

✗ Avoid When

Avoid for sites with APIs (use API instead), sites that explicitly forbid scraping, when data is copyrighted, or for login-required content without proper authorization.

Integration

  • Scheduled monitoring with change detection
  • Multi-source data aggregation pipelines
  • Fallback to web scraping when API rate limits hit
  • Headless browser for JavaScript-heavy sites

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.651 reviews
  • Benjamin Zhang· Dec 24, 2024

    We wired JinaAI into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Kaira Johnson· Dec 20, 2024

    According to our notes, JinaAI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Ganesh Mohane· Dec 8, 2024

    JinaAI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Mia Abebe· Dec 8, 2024

    JinaAI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Rahul Santra· Nov 27, 2024

    JinaAI is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Mia Iyer· Nov 27, 2024

    JinaAI is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Isabella Ghosh· Nov 15, 2024

    We evaluated JinaAI against two servers with overlapping tools; this profile had the clearer scope statement.

  • Meera Jain· Nov 11, 2024

    I recommend JinaAI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Pratham Ware· Oct 18, 2024

    We evaluated JinaAI against two servers with overlapping tools; this profile had the clearer scope statement.

  • Lucas Flores· Oct 18, 2024

    We evaluated JinaAI against two servers with overlapping tools; this profile had the clearer scope statement.

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