Oxylabs SERP MCP. Pull Structured Search Data From Global Engines.
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Oxylabs SERP scrapes structured search engine results from Google, Bing, Yandex, Baidu, and YouTube. It pulls parsed data—like organic rankings, shopping prices, hotel rates, or video metadata—without dealing with proxies, CAPTCHAs, or messy HTML parsing.
You send a query to your AI agent, and it gets clean JSON payloads for every major global search engine.
What your AI agents can do
Baidu search
Scrapes search results from Baidu, extracting structured data for market analysis in China.
Bing search
Runs an automated check on Microsoft Bing to gather structured organic and rich snippet search rankings.
Google images search
Pulls properties from Google Image searches, allowing you to analyze visual content trends for specific keywords.
Runs a search and pulls parsed results from all major Google features—including shopping listings, news headlines, and local travel details.
Allows you to run the same query against multiple engines (e.g., google_search vs. bing_search) to see how rankings vary by region or platform.
Retrieves specific structured data like current product prices using google_shopping_search, or hotel availability via google_travel_search.
Uses the youtube_search tool to get video metadata, views, and channel details from YouTube search results.
Accesses specialized sources like Google Scholar (google_scholar_search) for academic papers or Baidu (baidu_search) for specific regional market data.
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Oxylabs SERP: 10 Tools for Search Data Extraction
These ten tools allow you to systematically extract rankings, product properties, news headlines, and academic citations from the world's major search engines.
019d75edbaidu search
Scrapes search results from Baidu, extracting structured data for market analysis in China.
019d75edbing search
Runs an automated check on Microsoft Bing to gather structured organic and rich snippet search rankings.
019d75edgoogle images search
Pulls properties from Google Image searches, allowing you to analyze visual content trends for specific keywords.
019d75edgoogle news search
Gathers structured JSON data on breaking news results and syndication across global media sources.
019d75edgoogle scholar search
Extracts academic information from Google Scholar, pulling rich metadata about scholarly papers.
019d75edgoogle search
Performs the core search function for all major Google results (organic/snippets), returning structured data payloads.
019d75edgoogle shopping search
Retrieves explicit product listings and current pricing details from Google Shopping search results.
019d75edgoogle travel search
Lists structured rules for travel services, including hotel rates and availability data from Google Travel.
019d75edyandex search
Identifies precise search result arrays across native Yandex authentication domains for Russian market research.
019d75edyoutube search
Parses video rankings and channel details directly from YouTube search results into a structured format.
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What you can do with this MCP connector
Forget messing with proxies or writing messy HTML scrapers. This thing gives you clean, structured search engine results straight into your agent's payload. You send a query, and it spits back organized JSON for every major global search platform—no CAPTCHAs, no headache.
When you're working on Google stuff, you get everything in one place. You can run the core search using google_search to pull structured data from organic listings and rich snippets across all major results types. Need visuals? Use google_images_search to pull properties from image searches so you can analyze visual content trends for specific keywords.
For breaking news, google_news_search gathers structured JSON on global media syndication and current events. You don't have to guess about local services; google_travel_search lists structured rules for travel, including hotel rates and availability data. When you need product info or pricing, run google_shopping_search. For academic deep dives, you use google_scholar_search to extract rich metadata specifically about scholarly papers.
It's that detailed.
To compare how different regions rank things, the toolset lets you cross-check multiple platforms against the same query. You run a search on google_search and then immediately run it again using bing_search to see how Microsoft Bing ranks things versus Google's output. For specialized market analysis in China, you call baidu_search.
If your work focuses on Russia, yandex_search identifies precise search result arrays across native Yandex domains for that regional research angle.
For video content trends, don't just eyeball YouTube; use youtube_search. It parses video rankings and pulls channel details along with view counts directly from the search results into a structured format. You can combine all these niche sources—from analyzing academic papers via google_scholar_search to checking current product prices using google_shopping_search or tracking travel rates with google_travel_search—into one automated flow.
This whole setup is built for automation. You feed the query to your agent, and it handles the complexity of calling out to every single specialized search engine. It’s about getting clean, ready-to-process JSON payloads that let you immediately analyze how global rankings vary by platform or region.
How Oxylabs SERP MCP Works
- 1 First, subscribe to the server and enter your Oxylabs credentials.
- 2 Next, tell your AI agent which tool you need (e.g.,
google_shopping_search) and what query to run. - 3 The client executes the call; the server fetches the data from the search engine and returns it as clean, structured JSON.
The bottom line is: your AI agent sends a simple request, and you get back complex, organized search results that are ready for code.
Who Is Oxylabs SERP MCP For?
Anyone who relies on external data feeds—SEO professionals tracking keyword rank changes globally; market researchers needing real-time price monitoring across e-commerce sites; or content strategists analyzing what videos and news topics are trending right now. This is for people whose job depends on knowing the current state of a search result page, not just writing articles about it.
Uses google_search and yandex_search to track keyword performance across multiple international domains; they need clean data for ranking sheets.
Runs parallel searches using google_shopping_search or google_travel_search to compare competitor pricing or hotel rates in real time.
Analyzes trending topics by running youtube_search and google_news_search to determine optimal video content ideas for the next quarter.
What Changes When You Connect
- Stop building custom scrapers. The
google_searchtool handles the complexity of Google's constantly changing structure, giving you clean, predictable JSON every time. You just ask for 'organic results,' and it delivers. - Compare global visibility instantly. By using both
google_searchandyandex_search, you can see exactly how keyword rankings differ between major Western and Eastern markets in one workflow. - Monitor your competitors' pricing 24/7. The dedicated
google_shopping_searchtool lets you track product listings and price changes across multiple queries, automatically logging the data for comparison. - Analyze video trends with precision. Instead of just watching YouTube, use
youtube_searchto pull metadata—views, channel names, upload dates—to build a definitive content strategy report. - Get niche intelligence without extra steps. Need academic citation counts? Use
google_scholar_search. Want to check local news coverage? Callgoogle_news_search. All the data is structured and ready for analysis. - Handle regional diversity in one place. The inclusion of
baidu_searchandbing_searchmeans your agent isn't limited to Western search engines; it covers major global players.
Real-World Use Cases
Tracking a Competitor's Product Launch
A product manager needs to know if a competitor is listing their new widget on Google Shopping. They ask the agent to run google_shopping_search for 'widget brand X'. The agent returns a list of current listings, prices, and images in JSON format, showing exactly where the competitor is advertising.
Localizing an SEO Campaign
An agency needs to ensure their client's content ranks well in Moscow. They use yandex_search with local keywords; it provides structured results specific to the Russian market, which Google search tools simply can't provide.
Monitoring Academic Reputation
A researcher needs to gauge a scientist's impact. They run google_scholar_search on the scientist’s name. The agent pulls structured data, including citation counts and co-author details, allowing them to quantify academic influence immediately.
Planning an International Trip
A travel consultant needs quick pricing comparisons for a client trip. They use google_travel_search to get hotel rates in Tokyo, then run google_shopping_search to estimate the cost of local gadgets, all within one automated sequence.
The Tradeoffs
Copying/Pasting from Search Results
Manually going through Google and copying headline text or prices into a spreadsheet. This is slow, prone to formatting errors, and often incomplete.
→
Use the google_search tool; it extracts structured data points for headlines, snippets, and URLs directly into JSON format, bypassing manual copy-pasting entirely.
Limiting Yourself to One Engine
Running only Google searches when your target market is in China or Russia. You'll miss crucial regional ranking data.
→
Run parallel queries using both google_search and baidu_search, or use yandex_search. This ensures you capture the full global picture for keyword tracking.
Assuming All Data is Simple Text
Treating a Google Shopping result like a simple article—you only get the title, but lose critical data like current price or shipping estimate.
→
You must use google_shopping_search to access structured fields that contain pricing, product IDs, and vendor details. The tool is designed for machine-readable commerce data.
When It Fits, When It Doesn't
Use this server if your core task involves gathering multiple pieces of specific, structured data from varied search sources; i.e., you need to compare prices, track rankings across countries, or collect metadata (like view counts). The ability to run parallel searches via google_search, bing_search, and yandex_search is the main selling point.
Don't use this if your goal is simply to read a few articles or perform simple text summarization. If you just need general information, a standard LLM prompt is enough. But if that information needs to be systematically collected from multiple global domains and structured into machine-readable JSON, then this API suite is necessary.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Oxylabs SERP. 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.
Available Capabilities
Gathering competitive intelligence used to take hours of manual clicking.
Today, checking competitor pricing requires a painful cycle: open Google, search product X; copy the price and vendor name; move to Bing, repeat for product Y. Then you switch tabs to check their local news coverage on another site. This process is slow, messy, and always misses data points.
With this MCP server, your agent handles it all. You ask it to 'Find competitor pricing across three major platforms.' It runs `google_shopping_search`, `bing_search`, and `baidu_search` simultaneously, returning a single JSON object containing every price point you need.
Oxylabs SERP MCP Server: Get structured data from Google, Bing, and Yandex.
The biggest time sink is context switching. You have to open a spreadsheet for pricing, another tab for news, and yet another session just to check video trends on YouTube. Each piece of information lives in a different format and requires separate manual checks.
Here's the difference: your AI client calls `youtube_search` or `google_news_search`, and it gets clean data piped directly into your workflow. You don't manage tabs; you just get the final, structured answer.
Common Questions About Oxylabs SERP MCP
How do I track keyword rankings using google_search? +
You prompt your agent to run google_search with your target keyword. The result returns a JSON payload containing the top organic results, titles, and snippets, making it easy to parse and compare ranks.
Can I get shopping data from other regions besides Google Shopping? +
No, for structured e-commerce listings, you must use google_shopping_search. While tools like baidu_search scrape general results, they don't provide the specific product pricing structure.
Does yandex_search cover all global search engines? +
No. yandex_search is specific to Yandex, which covers the Russian market. For Western markets, you need to use google_search; for a comparison, run both tools.
What's the difference between google_scholar_search and google_search? +
google_search handles general web results (news, shopping, etc.). google_scholar_search targets academic sources, providing structured metadata specifically related to scholarly papers and citations.
How do I authenticate my AI client when using the `google_search` tool? +
You must provide your Oxylabs Username and Password credentials to the MCP server. Your AI agent uses these stored API keys to make requests, ensuring data extraction remains authorized for high-volume use.
Are there rate limits when running bulk queries using `google_shopping_search`? +
Yes, while the platform handles proxy rotation, usage is governed by API quotas. For extremely high volumes of requests, check your Oxylabs account dashboard for current throttling limits and necessary credits.
What structured data format does `google_travel_search` deliver for hotel listings? +
The tool returns a JSON payload containing explicit details. This structure includes the property name, star rating, nightly price, and user-submitted review scores, making it easy to process in your agent's code.
Can I combine search results from `baidu_search` with data from other global tools? +
Absolutely. You can run multiple tool calls sequentially or in parallel within a single workflow. This lets you compare metrics and structure insights drawn from disparate international sources.
Are the exported results structured JSON or raw HTML? +
The API returns highly structured, pre-parsed JSON. For example, a Google Search request directly returns organized arrays of organic results, related queries, ads, and knowledge panels — there's no need to write code to parse the HTML DOM.
Do I need to manage my own residential proxies? +
No. Oxylabs handles all infrastructure on the backend, automatically rotating residential proxies, fingerprinting, and bypassing CAPTCHAs. You just provide the search query.
Can I search Google News for PR tracking? +
Yes, the google_news_search tool natively queries the News tab, returning structured articles with publishers, titles, snippets, and exact publication timestamps, making it highly effective for PR and brand tracking.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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