Linkup (AI Search & RAG) MCP. Ground your AI with live, web-sourced data.
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Just plug in your AI agents and start using Vinkius.
Linkup (AI Search & RAG) connects your AI agents directly to real-time web intelligence. It lets you run semantic queries and pull clean, structured content from any URL or search result.
Stop relying on stale data; use Linkup when your agent needs facts that changed minutes ago—like market prices, breaking news summaries, or the latest technical documentation.
What your AI agents can do
Fetch url
Extracts clean text from a specific website URL, bypassing common bot protections and complex JavaScript loops.
Search web
Performs real-time web searches. You choose between 'fast' for basic facts or 'deep' for thorough research limits.
Use search_web to perform deep or fast searches and get immediate answers based on current web results.
Run fetch_url to pull clean, readable text directly from any provided URL, bypassing bot defenses.
Receive organized payloads including titles and source links by calling the search tools.
Run search_web with 'deep' mode to synthesize insights from numerous diverse web locations.
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Linkup (AI Search & RAG) MCP Server: 2 Tools
Use these two tools to execute real-time web searches and extract clean text from specific URLs for your AI agents.
019d75c8fetch url
Extracts clean text from a specific website URL, bypassing common bot protections and complex JavaScript loops.
019d75c8search web
Performs real-time web searches. You choose between 'fast' for basic facts or 'deep' for thorough research limits.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Linkup (AI Search & RAG), then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Linkup connects your AI agents straight into real-time web data. You're done relying on training data that’s already stale; this setup gives you immediate access to facts—the kind that changed minutes ago, like stock quotes or breaking news summaries.
When you need actionable context, the system works through two main channels: search_web and fetch_url. You call search_web when you need current information pulled from multiple sources. This tool performs real-time web searches, giving you organized payloads that include titles, snippets, and source links right off the bat. When you're just looking for quick facts, use the 'fast' setting; it handles basic queries efficiently.
But if you’re doing deep research—say, comparing technical specs across five different industry sites—you run search_web with the 'deep' limit. This mode synthesizes insights from numerous diverse web locations, building out a comprehensive picture that goes way beyond simple link lists.
fetch_url is what you use when you need to pull clean text directly from a single, specific website URL. It’s built tough; it bypasses the usual junk that breaks simpler scrapers, like complex JavaScript loops or basic bot protections. Instead of giving you messy HTML code full of ads and navigation menus, fetch_url strips all that noise away, leaving you with pure, readable content.
You can target specific documentation pages or niche technical sites and ensure your agent gets the exact context it needs without having to wade through irrelevant fluff.
The combination means you're not just getting search results; you’re getting structured data ready for immediate use. Whether you need a quick fact check via search_web's fast mode, or a detailed comparison across multiple sources using deep research, the platform handles it. If you know the exact page that holds the answer—a specific white paper or a product manual—you run fetch_url.
The output is clean text that your agent can interpret immediately for RAG pipelines.
When you need to ground your agent's answers in current facts, this system delivers it. You don't have to worry about the model hallucinating based on old data because every piece of context comes from a live pull—either through targeted extraction or deep search synthesis. The tool ensures that even if a website changes its underlying code structure, you still get clean text and structured source links.
It’s designed for maximum reliability when your agent's job depends on the information being current and accurate.
How Linkup (AI Search & RAG) MCP Works
- 1 Subscribe to this server and enter your Linkup API Key.
- 2 Your AI client sends a request, specifying whether it needs general facts (
search_web) or content from one URL (fetch_url). - 3 The server executes the tool call, retrieving real-time web data and passing clean context back to your agent.
The bottom line is: you give your AI an API key, and it gets live internet access for its responses.
Who Is Linkup (AI Search & RAG) MCP For?
Anyone whose job requires data that changes—market analysts needing current pricing, software engineers writing documentation on new APIs, or content teams tracking breaking news. If your AI agent relies on knowledge older than a week, this is for you.
Runs search_web to track competitor product announcements and pulls detailed analysis from multiple industry blogs.
Uses fetch_url to pull clean, current technical specifications directly from a source documentation URL for an API guide.
Automates large-scale data collection by running both search_web and fetch_url to build evaluation datasets that reflect real-world web content.
What Changes When You Connect
- Real-time answers are standard. Don't let your agent hallucinate dates or prices. Use
search_webto pull fresh facts from the moment of query execution. - Clean content extraction is automatic. When you run
fetch_url, the system strips away navigation and ads, giving your AI pure text ready for RAG indexing. - Search depth matters. Need a quick answer? Use 'fast' mode in
search_web. Need to write a literature review? Switch to 'deep' mode for thorough sourcing. - Structured data beats messy scraping. Both tools provide source URLs and titles, letting your agent know where the information came from.
- It works with what you already use. Connect Linkup to Claude, Cursor, or any MCP client to keep your workflow consistent.
Real-World Use Cases
Tracking breaking market news
A financial analyst needs the latest earnings summary for three different competitors. Instead of manually opening and reading multiple sites, they ask their agent to run search_web (deep mode). The agent synthesizes the key metrics from all sources in one response.
Building a technical documentation chatbot
An engineer wants their AI assistant to answer questions about an API whose docs live on a specific, complex website. They use fetch_url on the main documentation page. The agent then grounds its answers using only that clean, extracted text.
Verifying competitor claims
A market researcher needs to know if a competitor's claim about 'AI adoption rates in 2024' is accurate. They run search_web and ask the agent to cite its findings from diverse sources, getting multiple data points for comparison.
Generating content based on external reports
A writer needs a summary of an academic paper found online. They use fetch_url on the PDF's source page. The agent extracts the core text, allowing the writer to generate accurate summaries without manual copy-pasting.
The Tradeoffs
Relying only on internal knowledge bases
The agent gives an answer about a product launch that happened last week, but the company launched it yesterday. The LLM is operating on stale data.
→
Use search_web to pull current facts from external sources before answering. Always ground time-sensitive information in live web data.
Trying to parse a whole website manually
The user tries to copy and paste the entire raw HTML source of a documentation page into the prompt, resulting in massive noise (scripts, CSS, menus).
→
Use fetch_url on that site. It intelligently extracts only the readable article content, giving your agent clean context.
Using generic search for deep research
A user runs a basic web query and gets 10 surface-level results, but none of them contain the specific technical details they need.
→
Specify your intent when calling search_web. If you need detailed comparison, run it in 'deep' mode to force broader research limits.
When It Fits, When It Doesn't
Use this server if your core requirement is grounding the AI agent in external, real-time web context. This means facts that change (stock prices, breaking news) or documentation that lives online (API docs, manuals). If you need to analyze data only available inside a proprietary database (like an internal CRM), then this isn't for you; you need a dedicated API connection tool. Don't use it if your task is simply summarizing text already provided in the prompt. When in doubt, check if the information has a current web source—if it does, Linkup is what you need. Otherwise, stick to pure local processing.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Linkup. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 2 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Dealing with stale data and manual copy-pasting is tedious.
Today, if your agent needs current information, someone has to manually check Google, open the official site, navigate past the ads, find the right article, and then copy-paste that text into the chat. It's a slow, error-prone process.
With Linkup (AI Search & RAG), you skip all those steps. You call `search_web` or `fetch_url`, and your agent gets clean, validated context—all without you lifting a finger.
Linkup (AI Search & RAG) MCP Server: Live web intelligence in seconds.
You don't have to worry about bot protections or complex site structures. `fetch_url` handles the messy JavaScript and scraping difficulties, giving you usable text immediately.
Now your agent doesn't just 'guess' based on its training cut-off date; it answers using facts pulled live from the web.
Common Questions About Linkup (AI Search & RAG) MCP
How does Linkup (AI Search & RAG) handle private or gated content? +
It cannot access content behind paywalls or requiring specific logins. It operates by scraping publicly available web pages, so the source must be indexed and viewable online.
Is `fetch_url` better than general web search for documentation? +
Yes. If you have a direct URL to technical docs, use fetch_url. It guarantees clean text extraction from that single source, which is more reliable than synthesizing answers from multiple search snippets.
What's the difference between 'fast' and 'deep' in search_web? +
Fast mode is for quick fact-checking on common topics. Deep mode performs a much broader, more thorough crawl across diverse sources, which you need for comprehensive research.
Does Linkup (AI Search & RAG) only work with certain AI clients? +
No. As an MCP server, it connects to any compatible client that speaks the Model Context Protocol, including Cursor, Claude Desktop, and VS Code Copilot.
When running `fetch_url`, what kind of API credentials do I need to pass? +
You must provide a valid Linkup API Key for authentication. This key authorizes your AI agent to run the complex web scraping processes through our platform. It's the single credential that allows your client (like Claude or Cursor) to execute the extraction.
Does `search_web` output structured data, making it ready for a vector store? +
Yes, search_web retrieves more than just text. It delivers structured payloads that include titles, descriptive snippets, and source URLs. This format is specifically designed for seamless ingestion into vector databases.
How does `fetch_url` handle modern Single Page Applications (SPAs) with complex JavaScript? +
It bypasses advanced bot protections by automatically executing complicated SPA JavaScript loops. This means the tool renders dynamic content that standard scraping methods often miss, ensuring you get clean text regardless of the site's complexity.
Can I combine `search_web` and `fetch_url` in a single workflow? +
Absolutely. You first run search_web to identify high-relevance source URLs. Then, you pass the specific URL found during search directly into fetch_url for deep content extraction from that precise location.
How can Linkup help my agent provide more up-to-date answers? +
Use the linkup_search tool to give your agent access to live web data. By performing semantic searches across the internet, your agent can retrieve the latest news, reports, and documentation, grounding its answers in current facts.
Can I extract clean text from a specific URL for RAG? +
Yes. The linkup_fetch tool is specifically designed for content extraction. It renders the target page and returns a clean text version stripped of navigation and ads, making it ideal for feeding high-quality context to your agent.
What is the difference between standard and deep search modes? +
Standard search focuses on rapid fact-finding and top results. Deep search performs a more comprehensive crawl across many more sources, which is better for complex research tasks that require diverse perspectives and detailed data.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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