Exa MCP. Semantic search for concepts, not keywords.
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Exa. This server lets your AI agent search the web using semantic understanding, not just keywords. You can find articles or sites conceptually related to a topic, even if the exact words aren't there.
It lets you extract clean text and summaries from multiple specific URLs, or find competitors by analyzing the content of a known page.
It's for building knowledge bases and doing deep research.
What your AI agents can do
Exa find similar
Finds web pages that are conceptually similar to a URL you provide, useful for competitive research.
Exa get contents
Extracts clean text and summaries from a list of comma-separated URLs you specify.
Exa search
Performs a semantic web search using natural language, returning concepts and relevance scores, not just keywords.
Your agent finds conceptually relevant web results using natural language, going beyond simple keyword matching.
Your agent analyzes a given URL and returns web pages with semantically similar content for deep research or competitive checks.
Your agent accepts a list of comma-separated URLs and retrieves clean, structured text content from all of them.
Your agent runs searches using different modes (auto, instant, fast, deep) to prioritize speed or thoroughness.
Ask AI about this MCP
Supported MCP Clients
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019d7594exa find similar
Finds web pages that are conceptually similar to a URL you provide, useful for competitive research.
019d7594exa get contents
Extracts clean text and summaries from a list of comma-separated URLs you specify.
019d7594exa search
Performs a semantic web search using natural language, returning concepts and relevance scores, not just keywords.
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 Exa, 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
Listen up. This Exa server lets your AI agent do way more than just keyword searching. It uses semantic understanding, so your agent finds stuff that means what you're looking for, not just what has the right words. You're building knowledge bases and doing deep research with this thing.
Your agent can run a semantic web search using natural language, giving you concepts and relevance scores instead of just a list of keywords. You can control how deep the search goes by setting the mode—pick 'auto' for a good default, or 'deep' when you need maximum thoroughness.
Need to check out what the competition's up to? Give it a URL, and your agent runs exa_find_similar to pull back web pages that are conceptually similar. It's perfect for deep research or when you're doing competitive checks.
Got a bunch of sites you need content from? You just feed it a comma-separated list of URLs, and your agent uses exa_get_contents to pull out clean text and summaries from every single one. It strips out all the site garbage. Exa doesn't just search; it gives you the raw, usable info.
How Exa MCP Works
- 1 Subscribe to the server and provide your Exa API key to your AI client.
- 2 The agent calls a specific tool, passing the required input (e.g., a search query, a target URL, or a list of URLs).
- 3 Exa processes the request and returns the semantically relevant results, clean text, or similar links to your agent.
The bottom line is that your agent gets web search capabilities that understand meaning, not just words.
Who Is Exa MCP For?
The research scientist who needs to track down obscure academic papers or competitor white papers. The content strategist who needs to build a massive, structured knowledge base from dozens of sources. The technical architect building agents that need web context beyond simple API calls.
Runs exa_search to find related academic papers or market reports that simple keyword searches would miss. Then, it uses exa_get_contents to pull clean text from the top 5 results for a full literature review.
Runs exa_find_similar on a competitor's landing page URL to discover alternative content sources. It then uses exa_get_contents to aggregate key talking points from those sources.
Integrates exa_search into agent workflows, giving the agent the ability to perform contextual, deep web searches that inform decision-making.
What Changes When You Connect
- Find related content using
exa_find_similar. Instead of guessing adjacent topics, you point to a URL, and Exa gives you structurally similar pages for deep competitive analysis. - Build structured knowledge bases. Give your agent a list of sources, then use
exa_get_contentsto pull clean text and highlights from all of them at once. No more copy-pasting. - Improve search accuracy. When you run
exa_search, the agent understands the meaning of your query. It won't just find pages with the words 'AI agent'; it finds pages talking about agent concepts. - Control the search depth. Use the
exa_searchtool withdeepmode when you need the most thorough results, or switch toinstantmode if speed is the absolute priority. - Scale research efforts. You can run
exa_find_similarorexa_get_contentsacross dozens of URLs, making large-scale content discovery feasible for research teams.
Real-World Use Cases
Researching a new market segment
A market analyst needs to understand the full competitive space for 'multi-agent memory layers.' They ask their agent to run exa_search with the query. The agent returns 10 semantically relevant results, including niche blogs and white papers they would never find with Google.
Competitive content mapping
A content manager has a competitor's key product page URL. They ask the agent to run exa_find_similar on that URL. The agent returns 5 pages with similar content, allowing the manager to map out the competitor's entire content strategy.
Consolidating source material
A writer needs to create a briefing document based on three specific sources (an arXiv paper, a blog post, and a company research page). They feed the URLs into the exa_get_contents tool, and the agent returns clean, structured text and key highlights from all three in one pass.
Building an agent's knowledge base
An AI developer wants an agent to always have access to the latest industry standards. They set up a workflow using exa_search to query 'latest industry standards for LLMs' and then use exa_get_contents to pull the text from the top 5 results, making the knowledge base current.
The Tradeoffs
Treating Exa like a keyword search
A user asks the agent to search for 'decentralized LLM infrastructure' but only includes the words 'decentralized' and 'LLM'. The agent gets results that only contain those two words, missing the conceptual context of 'infrastructure.'
→
Instead, let your agent run exa_search using the full natural language phrase: 'companies building decentralized LLM infrastructure.' This forces Exa to find pages that discuss the entire concept, not just the keywords.
Trying to read content page-by-page
The user finds 5 useful URLs and manually has to open each one, copy the text, and paste it into a document. This is slow and messy, and they might miss key sections.
→
Pass all 5 URLs to the exa_get_contents tool. It pulls the clean text and key highlights from every single link automatically, giving you a consolidated source document.
Searching without context
The user only provides a single, vague URL and asks the agent to 'find similar stuff.' The results are too broad or irrelevant because the agent can't narrow the focus.
→
First, use exa_search to establish the general topic. Then, feed that query into exa_find_similar to narrow down the search and find highly related content.
When It Fits, When It Doesn't
Use Exa if your process requires understanding the meaning or concept behind the data, not just the literal words. This is for deep research, competitive analysis, or building knowledge bases from multiple sources.
Don't use Exa if you only need to verify a single, known fact or pull text from a single, specific document. For simple, single-query lookups, a standard database lookup or a dedicated API endpoint might be faster.
If your goal is to find related concepts, use exa_find_similar. If you need to synthesize information from a known set of links, use exa_get_contents. If you need to find new, related topics based on a general query, use exa_search.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Exa. 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 3 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sifting through web pages for context is a manual nightmare.
Right now, if you need to build a knowledge base from, say, five different sources—an academic paper, a blog, a technical spec—you have to open five browser tabs. You manually copy the key paragraphs, you paste them into a doc, and you spend hours cleaning up formatting and figuring out which quote belongs where. It’s slow, it’s tedious, and you lose context in the mess.
With the Exa MCP Server, you just give the agent the list of URLs. The agent uses `exa_get_contents` and returns clean, structured text and key highlights from all five sources, perfectly organized. You skip the tabs, the copy-pasting, and the formatting headache.
Exa MCP Server: Get web context from a single query.
Before Exa, if you wanted to know about 'federated learning architectures,' you had to use simple search, which would only return results with those exact words. You'd miss the deep articles discussing the concept, even if they used different terminology.
Now, your agent uses `exa_search`. It understands that 'federated learning' means a certain type of distributed compute. It finds the articles discussing that concept, regardless of whether they use the precise keywords. It's a fundamental shift from searching to understanding.
Common Questions About Exa MCP
How does exa_search differ from a standard Google search? +
Exa uses semantic search. It understands the meaning behind your query, not just the words. This means it will surface results that are conceptually related to your topic, even if the page doesn't use your exact keywords.
Can exa_find_similar find competitors? +
Yes. You give it a competitor's URL, and it returns pages with similar content. This is useful for mapping out a competitor's content strategy or finding alternative sources.
Does exa_get_contents handle complex formatting? +
It extracts clean text, highlights, and summaries. It strips away the surrounding junk—ads, sidebars, navigation—so you get usable content ready for your knowledge base.
What search types are available in exa_search? +
The exa_search tool supports auto (default), instant (fastest), fast, and deep (most thorough) search types. You select the depth based on whether you need speed or maximum detail.
How do I use exa_find_similar for research? +
Provide the URL of a foundational article or paper. The agent will then find other web pages that discuss similar concepts, expanding your research scope beyond the original source.
What format does exa_get_contents expect for its URLs? +
It expects a comma-separated list of URLs. You provide the list, and the tool retrieves the clean text content from each specific source.
What happens if I use exa_search with a query that is too vague? +
The tool will return semantically relevant results based on the concepts present in your query. If the query is too vague, the results will cover the broadest conceptual area it detects.
Are there any limitations or rate limits when calling exa_find_similar? +
The server provides 1,000 free searches per month. Beyond that, you'll need to check your account dashboard for available credits or plan upgrades.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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