Exa MCP. Find web context using meaning, not just words.
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Exa is an advanced semantic search MCP that finds web content based on meaning, not keywords. It lets your AI client query the entire web for context, extract clean text from specific URLs, and find articles semantically similar to a known document.
Stop searching by topic; start asking questions and getting actionable data.
What your AI agents can do
Answer
Requests the MCP to provide an AI-generated answer based on web search results for general queries.
Find similar
Finds multiple web pages that are conceptually related to a provided URL, useful for competitive analysis.
Find similar with contents
Identifies semantically similar pages and includes their actual readable text content in the results.
The MCP executes advanced semantic searches, understanding the intent of a query rather than matching isolated keywords.
It locates web articles or pages that share deep conceptual similarity with a specific starting URL.
The MCP pulls clean, readable text and metadata from multiple URLs in a single request.
You can narrow the search scope to look only at defined websites or documentation sets.
It performs searches specifically tuned for trending news and recently published content.
Ask AI about this MCP
Supported MCP Clients
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Exa: 10 Web Intelligence Tools
These tools let your agent perform everything from basic keyword searches to advanced neural concept mapping and content extraction across the web.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Exa on Vinkius019dd0ecanswer
Requests the MCP to provide an AI-generated answer based on web search results for general queries.
019dd0ecfind similar
Finds multiple web pages that are conceptually related to a provided URL, useful for competitive analysis.
019dd0ecfind similar with contents
Identifies semantically similar pages and includes their actual readable text content in the results.
019dd0ecget contents
Pulls out clean, structured body text and metadata from one or more given web page IDs.
019dd0ecsearch
Performs a standard AI-powered search across the web, returning titles, URLs, and relevance scores for matching pages.
019dd0ecsearch domain
Limits a general search query to only look within a specified website or domain name.
019dd0ecsearch keyword
Executes a basic, traditional keyword match search across the web's indexed content.
019dd0ecsearch neural
Runs an advanced semantic search ideal for complex concepts and research topics where keywords might fail.
019dd0ecsearch recent
Filters web searches to only return content that has been published or updated within the last few days.
019dd0ecsearch with contents
Combines searching and extraction by returning both relevant links and their cleaned text content simultaneously.
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 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ 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
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 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Sifting Through Web Data Is a Nightmare
Today, if you need to research a new topic, your workflow looks like this: You run a Google search. You get 20 links. You click the top five. Then, for each link, you have to copy and paste sections of text into a document to prove the point. This process is slow, tedious, and requires constant manual switching between tabs.
With Exa MCP, your agent handles this mess automatically. Instead of clicking through pages, you tell it what you're looking for—by concept or by site—and it pulls clean, usable text directly into your workflow. You get actionable context without the painful copy-paste cycle.
Semantic Search and Content Extraction
The manual steps that disappear are running multiple searches (keyword first, then semantic), manually identifying similar sources, and finally gathering the text content from every single one. This takes time just to curate the raw material.
Now, you define your goal: 'Find articles about X related to Y.' The MCP handles the entire pipeline—the deep search, the similarity check, and the clean extraction. You get a structured data payload ready for analysis.
What you can do with this MCP connector
Need to research something across dozens of sources? Exa helps your agent understand the intent behind a question, returning high-quality results even when you don't know the exact keywords. Instead of relying on standard searches that only match terms, this MCP uses neural embeddings to find web pages and articles that are conceptually similar to what you need.
You can ask it to search within a specific domain for targeted documentation or pull clean text from multiple URLs at once. Since your keys pass through a zero-trust proxy when communicating with Exa, the credentials are used only in transit—they never sit on disk. This setup lets you build complex workflows that gather web data contextually before passing it to other tools in your agent chain.
019dd0ec-cc9c-72a3-9cf4-c1cd808fcfea 
How Exa MCP Works
- 1 Connect your AI client to this MCP using your Exa API key.
- 2 Instruct your agent to perform a search, specifying whether the query needs semantic context or if you are searching within a confined domain.
- 3 The MCP executes the search and returns structured data containing titles, URLs, relevance scores, and extracted content.
The bottom line is you get structured web intelligence directly into your agent's workflow, eliminating manual copy-pasting of research findings.
Who Is Exa MCP For?
This MCP is for the technical researchers and content strategists who spend their days drowning in raw web data. It solves the pain point of needing contextually accurate information across dozens of disparate sources, rather than just keyword matches.
Uses find_similar and search_neural to locate conceptual gaps in existing literature or find adjacent topics for a paper.
Runs batch content extraction using get_contents on competitor articles, then uses search_domain to check internal knowledge bases for coverage gaps.
Uses search_domain and search_with_contents to quickly verify the latest documentation or find specific implementation details buried deep in a corporate site.
What Changes When You Connect
- Stop relying on simple keyword matching. Use
search_neuralto find articles that match the intent of your query, even if they use different terminology. - When you need deep research, pair
find_similarwithget_contents. This lets you identify related sources and immediately extract their raw text for analysis. - For documentation work, always start with
search_domain. It scopes the search to a specific site, preventing irrelevant noise from pulling your agent off course. - If timing matters, use
search_recentinstead of general search. This cuts out stale results and focuses only on current industry breakthroughs. - The best approach is often using
search_with_contents. It executes the web lookup and pulls the usable text in one step, saving multiple API calls.
Real-World Use Cases
Assessing a Competitor's Strategy
A marketing manager wants to know what key publications are covering articles similar to theirs. They run find_similar against their own blog post URL, quickly identifying five other sources that hit the same semantic notes.
Building a Technical Knowledge Base
An engineer needs to compile all internal documentation on 'Authentication Flow' from three different site sections. They use search_domain repeatedly across the company wiki, then pass all resulting URLs to get_contents for a single, clean text dump.
Tracking Market Buzz
A financial analyst needs to know what breakthroughs happened this week. They use search_recent with 'quantum computing' and then pass the resulting links to answer to get a summarized overview of the current market conversation.
Validating Research Claims
A student needs background material for a paper on 'pre-print physics'. They run search_neural to gather initial concepts, and then use search_with_contents on the top results to ensure they can pull accurate supporting text.
The Tradeoffs
Using general search for deep research
Searching 'machine learning ethics' via a basic web search only returns articles that mention those exact three words, missing deeper conceptual discussions.
→
Instead, use search_neural. This finds pages semantically related to machine learning ethics, pulling in relevant academic papers and white papers even if they don't contain the precise keyword phrase.
Copy-pasting URLs manually
Finding three important articles across different sites, then having to open them one by one to copy chunks of text for your report.
→
Use find_similar first to locate the critical source URLs, and then pass those links as a batch list to get_contents. This extracts all the clean body text in a single call.
Ignoring site boundaries
Running a general search for 'API documentation' that pulls results from random blogs, making it impossible to trust the source material.
→
Always use search_domain and specify the exact company website. This restricts all search effort to the defined, authoritative source.
When It Fits, When It Doesn't
Use this MCP if your primary need is contextual web intelligence: you require understanding meaning or pulling clean data from external sources. For example, use find_similar when you want related concepts, not just similar words. However, don't use it if your goal is merely to find a specific factoid (e.g., 'What is the capital of France?'). In that case, a simple question-answering tool might suffice. If you only need basic keyword hits on a known site, search_keyword is faster and less resource-intensive than running a full semantic search through this MCP.
Common Questions About Exa MCP
How does Exa MCP handle conceptual searches? Use `search_neural`. +
search_neural powers advanced semantic search. It understands that 'big cats' and 'jaguars' mean the same thing, so it finds relevant articles even if they never use the exact keywords you provide.
Can Exa MCP only pull text from one page? +
No. You can pass multiple URLs to get_contents, and the MCP will extract clean, readable body text and metadata for every single page ID in that batch request.
Is there a way to limit my search results to one company's site? +
Yes. Use search_domain and provide the specific domain name. This locks your entire search effort down, so you only get results from that single source.
What is the difference between `search` and `search_with_contents`? +
search returns links and scores for matching pages. search_with_contents does both: it gives you the relevant links and extracts a snippet of clean content from those results immediately.
When I run a broad query using `search`, what kind of metadata does it return? +
The search results provide comprehensive data beyond just titles and URLs. You get relevance scores and specific resource IDs, which lets your agent instantly rank the quality of sources for you.
Does my API key need to be refreshed every time I use `find_similar`? +
No, Vinkius handles credential security. Your keys pass through a zero-trust proxy and are never saved on disk. You connect your AI client once for access.
If my workflow needs to run many times, is there a limit when using `search_keyword`? +
Vinkius manages underlying rate limits across all MCP calls. If you hit a cap, the agent receives an explicit error message, which allows your calling application to manage retries.
Can I combine `find_similar` and `get_contents` in one automation? +
Yes, you can chain them. First, use find_similar to get a list of related URLs, and then feed those specific links into get_contents to extract the clean text bodies automatically.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.