# Exa MCP for AI Agents AI Agent Connect > Exa is a semantic search engine built for AI. It finds conceptually relevant web content instead of just matching keywords. By using neural search technology, it helps your agent understand the meaning behind your queries to find the most relevant information for research, content discovery, and competitive analysis. It allows your agent to find similar pages, extract clean text from URLs, and perform deep web searches that prioritize meaning over exact word matches. ## Overview - **Category:** ai-frontier - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_yX0o3kbQylbPvraZb07fJ4pytT29vYl6Z1kPSXHZ/ai-agent-connect - **Tags:** neural-search, semantic-similarity, web-data, contextual-search, content-curation, ai-search ## Description Exa lets you give your agent the ability to browse the web with actual understanding. Instead of just looking for keywords, your agent can identify the underlying concepts of a query to find the right information. This changes how you handle research and data gathering. You can ask for academic papers on a specific topic and get results that actually matter, even if they don't use the exact words you typed in your prompt. You can also point your agent at a single URL and have it find every other page on the web that covers similar ground. This is a massive shift for anyone who needs to build knowledge bases, perform deep competitive analysis, or conduct thorough market research. By connecting this to your workflow through the Vinkius catalog, you give your agent a way to pull clean text from dozens of links at once. It handles the heavy lifting of scraping and highlighting so you can focus on the actual data instead of clicking through dozens of tabs. You get a way to source high-quality, relevant content without the noise of standard search engines, making it easier to find the needles in the digital haystack. It bridges the gap between a simple search query and a deep understanding of what you're actually looking for. Whether you're looking for a specific niche in the tech space or trying to map out a competitor's entire content strategy, this Connector provides the context needed to get there quickly. You no longer have to sift through pages of irrelevant results that happen to contain your keywords. Instead, you get a curated list of sources that actually align with your goals. It turns the web into a structured resource that your agent can actually navigate and understand. ## Tools ### answer Get an AI answer ### find_similar Great for competitive analysis and research. Find similar pages ### find_similar_with_contents Find similar pages with content ### get_contents Extract page contents ### search_domain Useful for site-specific research or documentation lookups. Search within a domain ### search_keyword Traditional keyword search ### search_neural Ideal for conceptual queries and research topics. Neural semantic search ### search_recent Ideal for news and trending topics. Search recent content ### search Returns titles, URLs, and relevance scores for matching pages. Search the web with AI ### search_with_contents Search and extract page content ## Prompt Examples **Prompt:** ``` Search for companies building memory infrastructure for AI agents. ``` **Response:** ``` Found 10 semantically relevant results: | Company | Website | Relevance Score | | :--- | :--- | :--- | | **Mem0** | mem0.ai | 0.94 | | **Zep** | getzep.com | 0.91 | | **Letta** | letta.com | 0.88 | **Summary:** These companies are leaders in providing long-term memory and temporal knowledge graphs for agentic workflows. ``` **Prompt:** ``` Find pages similar to https://docs.langchain.com/docs/get_started/introduction ``` **Response:** ``` Found 10 pages similar to the LangChain introduction: * **LlamaIndex Documentation** (docs.llamaindex.ai) — Score: 0.92 * **CrewAI Getting Started** (docs.crewai.com) — Score: 0.89 * **AutoGen Documentation** (microsoft.github.io/autogen) — Score: 0.86 * **Haystack Introduction** (docs.haystack.deepset.ai) — Score: 0.83 ``` **Prompt:** ``` Extract the content from these 3 URLs: https://arxiv.org/abs/2401.00001, https://openai.com/blog, https://anthropic.com/research ``` **Response:** ``` ### 1. arxiv.org — Research Paper > **Key highlights:** Novel approach to multi-agent coordination using graph neural networks. ### 2. openai.com — Blog Post > **Full text extracted:** 4,200 words covering latest research developments... ### 3. anthropic.com — Research Page > **Highlights:** Constitutional AI methodology and safety-first approach... ``` ## Capabilities ### Find pages with similar meaning Your agent can identify and list web pages that match the conceptual theme of a specific URL. ### Extract clean text from URLs Provide a list of links and have your agent pull out the relevant text and highlights automatically. ### Perform semantic web searches Your agent can search the web using natural language to find results based on meaning rather than just keywords. ### Get relevance scores Receive scores that tell your agent how well a search result actually matches the intent of the query. ### Discover related research Find connected articles and resources that keyword-based tools often miss. ## Use Cases ### Finding research papers on niche AI topics A researcher asks for papers on 'memory infrastructure for agents' and gets papers that match the concept, even if they don't use that exact phrase. ### Competitor discovery A content strategist points the agent at a competitor's blog and asks for similar sites to see who else is in the space. ### Building a knowledge base A user provides 20 URLs and asks the agent to extract the main highlights from all of them to create a summary. ### Contextual web search An agent needs to find the best way to implement a specific coding pattern and uses semantic search to find relevant tutorials. ## Benefits - Get results based on meaning using `exa_search` instead of just matching words. This helps your agent find relevant information even when the user doesn't use the exact right terminology. - Quickly build knowledge bases by pulling clean text with `exa_get_contents`. You can provide a list of URLs and get back structured data without having to manually scrape every page. - Expand your research scope by finding similar pages with `exa_find_similar`. This is perfect for finding competitors or related articles that aren't linked on the main site. - Reduce noise in search results by focusing on high-relevance scores. Your agent gets a more accurate picture of the web because it understands the context of the search. - Automate content discovery for competitive analysis without manual browsing. You can point your agent at one URL and have it find every other page on the web that covers similar ground. ## How It Works The bottom line is your agent gets a way to understand the web like a human does. 1. Subscribe to the Exa MCP on the Vinkius Marketplace. 2. Add your Exa API key to your AI client. 3. Ask your agent to search the web or find similar content. ## Frequently Asked Questions **How does Exa help my AI agent search the web?** Exa gives your agent a way to understand the meaning behind your questions. Instead of just looking for keywords, it finds content that actually matches the concept of what you're looking for. **Can I use Exa to find competitors for my business?** Yes, you can point your agent at a competitor's website and ask it to find similar pages. This helps you discover related services and content across the web. **What is the difference between Exa and a standard search?** Standard search looks for exact word matches. Exa uses semantic understanding to find results that are relevant to your intent, even if they don't use your specific words. **How do I get clean text from multiple URLs at once?** You can provide a list of links to the agent, and it will use the extraction tools to pull out the relevant text and highlights into a structured format for you. **Can Exa find research papers that don't have specific keywords?** Yes, that is one of its primary strengths. Because it understands concepts, it can find relevant research papers based on the subject matter rather than just specific terminology. **Is Exa good for building a knowledge base?** It is excellent for this. You can use it to gather clean text and highlights from a curated list of sources to build a structured knowledge base for your agent. **How is Exa different from Google Search?** Exa uses neural embedding models to understand the meaning of your query, not just keywords. When you search 'startups building AI infrastructure', Google returns pages containing those exact words. Exa returns companies that match that concept — even if their pages use completely different terminology. It also provides specialized search indexes for people, companies, code, and academic papers. **What does the free tier include?** Exa's free tier includes 1,000 searches per month with content extraction included for up to 10 results per search at no additional cost. No credit card required. For higher volumes, paid plans offer increased quotas and priority support. **Can I filter results by domain, date, or category?** Yes! Exa supports powerful filtering options: include/exclude specific domains, filter by publication date range, search within specific categories (news, blog, academic, etc.), and even use domain path filtering to search within specific sections of a website. **Can I perform a meaning-based search instead of keywords via AI?** Yes! Use the `search` tool. Provide your query and enable `useAutoprompt`. Exa will use its neural embeddings to find links that match the intent of your question. **How do I find articles similar to a specific URL?** Use the `find_similar` tool. Provide the seed URL, and the agent will retrieve a list of pages that are semantically related to that content. **Is it possible to extract cleaned text from multiple pages via AI?** Absolutely. Use the `get_contents` query. Provide an array of URLs or Exa IDs, and the agent will return the cleaned text content and metadata for each page.