# Jina Reader — AI-Friendly Web Content Extraction > Convert any URL to clean markdown for AI consumption. Free API at r.jina.ai strips ads, navigation, and clutter. Used by AI agents for web research and RAG. ## Install Merge the JSON below into your `.mcp.json`: ## Quick Use ```bash # Prefix any URL with r.jina.ai curl https://r.jina.ai/https://example.com # Or use the API curl -H "Accept: text/markdown" https://r.jina.ai/https://github.com/jina-ai/reader ``` Returns clean markdown — no ads, no navigation, no clutter. ## What is Jina Reader? Jina Reader is a free API that converts any web page into clean, AI-friendly markdown. Simply prefix a URL with `r.jina.ai/` and get back structured content stripped of ads, navigation, scripts, and visual clutter — perfect for feeding into LLMs, RAG pipelines, and AI agents. **Answer-Ready**: Jina Reader is a free API that converts any URL to clean markdown for AI consumption. Prefix any URL with `r.jina.ai/` to strip ads, navigation, and clutter. Used by AI agents and RAG pipelines for web research. 20k+ GitHub stars. **Best for**: AI agent developers who need web content extraction. **Works with**: Any HTTP client, LangChain, LlamaIndex, Claude Code. **Setup time**: Zero — just use the URL prefix. ## Core Features ### 1. URL-to-Markdown ```python import requests url = "https://r.jina.ai/https://docs.anthropic.com/en/docs/agents" response = requests.get(url, headers={"Accept": "text/markdown"}) markdown = response.text # Clean markdown ready for LLM consumption ``` ### 2. Search Integration ```bash # Search the web and get markdown results curl https://s.jina.ai/best+AI+coding+tools+2026 ``` Returns search results as structured markdown with titles, URLs, and content snippets. ### 3. Options ```bash # Get only main content (no headers/footers) curl -H "X-Return-Format: markdown" \ -H "X-No-Cache: true" \ https://r.jina.ai/https://example.com # With images as markdown curl -H "X-With-Images: true" \ https://r.jina.ai/https://example.com ``` ### 4. Grounding for AI Agents Use as a tool for AI agents to research the web: ```python def web_read(url: str) -> str: resp = requests.get(f"https://r.jina.ai/{url}") return resp.text # Agent can now read any web page content = web_read("https://docs.python.org/3/library/asyncio.html") ``` ### 5. Batch Processing ```python urls = [ "https://r.jina.ai/https://page1.com", "https://r.jina.ai/https://page2.com", "https://r.jina.ai/https://page3.com", ] # Process in parallel for RAG ingestion ``` ## Use Cases | Use Case | How | |----------|-----| | RAG Pipeline | Extract clean text from source URLs | | AI Research Agent | Read documentation and articles | | Content Scraping | Get structured content without Playwright | | Summarization | Clean input for LLM summarizers | ## Rate Limits | Tier | Rate | Price | |------|------|-------| | Free | 20 RPM | $0 | | Paid | 200 RPM | From $10/mo | ## FAQ **Q: Is it really free?** A: Yes, 20 requests per minute free. No API key required for basic use. **Q: How does it handle JavaScript-rendered pages?** A: Jina Reader renders JavaScript before extraction, handling SPAs and dynamic content. **Q: Can I self-host?** A: Yes, the reader is open-source and can be self-hosted. ## Source & Thanks > Created by [Jina AI](https://github.com/jina-ai). Licensed under Apache 2.0. > > [jina-ai/reader](https://github.com/jina-ai/reader) — 20k+ stars ## 快速使用 ```bash curl https://r.jina.ai/https://example.com ``` 任何 URL 前加 `r.jina.ai/` 即获得干净的 Markdown。 ## 什么是 Jina Reader? Jina Reader 是免费 API,将任何网页转为 AI 友好的干净 Markdown。去除广告、导航和杂乱元素。 **一句话总结**:Jina Reader 是免费 API,将任何 URL 转为干净 Markdown,供 AI 代理和 RAG 管线消费,20k+ GitHub stars。 **适合人群**:需要网页内容提取的 AI 代理开发者。**安装时间**:零配置。 ## 核心功能 ### 1. URL 转 Markdown 前缀任何 URL 获取结构化内容。 ### 2. 搜索集成 `s.jina.ai/` 前缀搜索网页并返回 Markdown 结果。 ### 3. JS 渲染 自动渲染 JavaScript,处理 SPA 和动态内容。 ### 4. 可自托管 开源可自部署。 ## 常见问题 **Q: 真的免费?** A: 是,每分钟 20 次请求免费,无需 API Key。 **Q: 支持 JS 渲染页面吗?** A: 支持,提取前先渲染 JavaScript。 ## 来源与致谢 > [jina-ai/reader](https://github.com/jina-ai/reader) — 20k+ stars, Apache 2.0 --- Source: https://tokrepo.com/en/workflows/9c6cbf5f-e46b-40d6-aaf6-d2a4d5a0e657 Author: MCP Hub