JinaAI Search▌
by spences10
Enhance web content discovery with JinaAI Search, integrating ai search like bing ai and Microsoft for advanced data ext
Integrates JinaAI's search capabilities for web content discovery, information retrieval, and data extraction
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / LLM applications needing web search
- / Content research and information retrieval
- / Documentation and knowledge gathering
capabilities
- / Search web content through JinaAI API
- / Extract clean text with preserved structure
- / Gather images and links from web pages
- / Control response size with token budgets
- / Cache search results for performance
what it does
Searches the web using JinaAI's API to retrieve clean, LLM-optimized content from web pages and documentation. This repository is no longer maintained - use mcp-omnisearch instead.
about
JinaAI Search is a community-built MCP server published by spences10 that provides AI assistants with tools and capabilities via the Model Context Protocol. Enhance web content discovery with JinaAI Search, integrating ai search like bing ai and Microsoft for advanced data ext It is categorized under search web.
how to install
You can install JinaAI Search 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 Search 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-search
⚠️ 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This repository is no longer maintained.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 Search API with LLMs. This server provides efficient and comprehensive web search capabilities, optimised for retrieving clean, LLM-friendly content from the web.
<a href="https://glama.ai/mcp/servers/u6603w196t"> <img width="380" height="200" src="https://glama.ai/mcp/servers/u6603w196t/badge" /> </a>Features
- 🔍 Advanced web search through Jina.ai Search API
- 🚀 Fast and efficient content retrieval
- 📄 Clean text extraction with preserved structure
- 🧠 Content optimised for LLMs
- 🌐 Support for various content types including documentation
- 🏗️ Built on the Model Context Protocol
- 🔄 Configurable caching for performance
- 🖼️ Optional image and link gathering
- 🌍 Localisation support through browser locale
- 🎯 Token budget control for response size
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-search": {
"command": "node",
"args": ["-y", "mcp-jinaai-search"],
"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-search": {
"command": "wsl.exe",
"args": [
"bash",
"-c",
"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-search"
]
}
}
}
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:
search
Search the web and get clean, LLM-friendly content using Jina.ai Reader. Returns top 5 results with URLs and clean content.
Parameters:
query(string, required): Search queryformat(string, optional): Response format ("json" or "text"). Defaults to "text"no_cache(boolean, optional): Bypass cache for fresh results. Defaults to falsetoken_budget(number, optional): Maximum number of tokens for this requestbrowser_locale(string, optional): Browser locale for rendering contentstream(boolean, optional): Enable stream mode for large pages. Defaults to falsegather_links(boolean, optional): Gather all links at the end of response. Defaults to falsegather_images(boolean, optional): Gather all images at the end of response. Defaults to falseimage_caption(boolean, optional): Caption images in the content. Defaults to falseenable_iframe(boolean, optional): Extract content from iframes. Defaults to falseenable_shadow_dom(boolean, optional): Extract content from shadow DOM. Defaults to falseresolve_redirects(boolean, optional): Follow redirect chains to final URL. Defaults to true
Development
Setup
- Clone the repository
- Install dependencies:
pnpm install
- Build the project:
pnpm run build
- Run in development mode:
pnpm run dev
Publishing
- Create a changeset:
pnpm changeset
- Version the package:
pnpm version
- Build and publish:
pnpm release
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
MIT License - see the LICENSE file for details.
Acknowledgments
- Built on the Model Context Protocol
- Powered by Jina.ai Search API
FAQ
- What is the JinaAI Search MCP server?
- JinaAI Search 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 Search?
- This profile displays 70 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.Install web automation MCP server via npm or pip
- 2.Configure allowed domains and rate limits in MCP config
- 3.Test with simple fetch: 'Get content from example.com'
- 4.Progress to extraction: 'Extract all product prices from this page'
- 5.Set up monitoring: 'Check this URL daily for changes'
- 6.Parse structured data: 'Create CSV from this table'
- 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
4.6★★★★★70 reviews- ★★★★★Carlos Srinivasan· Dec 24, 2024
JinaAI Search is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Maya Sharma· Dec 24, 2024
JinaAI Search is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Kabir Jain· Dec 12, 2024
We wired JinaAI Search into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Ganesh Mohane· Dec 4, 2024
I recommend JinaAI Search for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Anika Diallo· Dec 4, 2024
JinaAI Search reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Jin Kapoor· Nov 19, 2024
JinaAI Search reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Anaya Anderson· Nov 15, 2024
Useful MCP listing: JinaAI Search is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Jin Ghosh· Nov 15, 2024
We evaluated JinaAI Search against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Sakura Abebe· Nov 15, 2024
I recommend JinaAI Search for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Alexander Khan· Nov 11, 2024
I recommend JinaAI Search for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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