SERPHouse MCP. Pull live data from Google and Bing search results.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
SERPHouse connects your AI agent directly to Google and Bing’s real-time search results. It lets you scrape organic content, dynamic pricing, images, news, scholarly articles, and more without running into Captcha blocks or relying on stale training data.
Your agent gets live, proxy-rotated access to the actual SERP metadata.
What your AI agents can do
Bing images
Searches for images across the Bing platform.
Bing news
Retrieves current news articles from Bing.
Bing search
Performs a standard search query on Bing.
The agent scrapes dynamic pricing and specific product listings by calling the google_shopping tool.
You query for scholarly articles, abstracts, and citation counts using the google_scholar tool.
The agent separates search results to pull only images (via google_images) or videos (via google_videos).
You check current headlines and localized stories by running the bing_news or google_news tools.
The agent runs general searches using parameters like location or language via google_search.
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Supported MCP Clients
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SERPHouse MCP Server: 11 Tools for Search & Data Extraction
Use these tools to perform targeted searches across Google and Bing—from product listings and news feeds to scholarly articles.
019d7606bing images
Searches for images across the Bing platform.
019d7606bing news
Retrieves current news articles from Bing.
019d7606bing search
Performs a standard search query on Bing.
019d7606get account info
Pulls specific account details associated with the SERPHouse service.
019d7606google images
Searches for images across the Google platform.
019d7606google news
Retrieves current news articles from Google.
019d7606google scholar
Performs focused searches on academic and scholarly publications via Google Scholar.
019d7606google search
Runs a general search on Google, supporting advanced parameters like location or language codes.
019d7606google shopping
Searches for and extracts product listings and pricing data from Google Shopping.
019d7606google videos
Searches for video content across the Google platform.
019d7606list locations
Lists all location codes and supported geographical regions that can be used in queries.
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.
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Make Your AI Do More
Start with SERPHouse, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
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- Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector
Your AI agent connects straight to Google and Bing's live search endpoints, bypassing stale data sets and anti-bot blocks. It gives your client real-time access to SERP metadata using proxy rotation—you won't hit a Captcha wall when you need the data.
Product Data Extraction
When you gotta know what something costs right now, use google_shopping. Your agent scrapes product listings and pulls dynamic pricing directly from Google Shopping results. It extracts specific details for multiple items across different sellers in one go. You can cross-reference price points instantly without leaving the chat window.
Academic Research
Need to deep dive into scholarly material? Just run google_scholar. This tool handles focused searches specifically for academic publications. Your agent pulls abstracts, citation counts, and full details on peer-reviewed articles, letting you build a research dossier faster than any human could manually search.
General Search & Scope Control
For general queries, start with google_search. It runs standard Google searches but lets you specify advanced parameters like location or language codes. If you need to know which areas are supported, use list_locations first. When you're ready for a quick check on Bing, bing_search handles the same thing.
Media and Visual Content
If you're looking at visuals, your agent separates out only what you want. To pull images from Google, call google_images; for Bing, use bing_images. If you need video content, you run google_videos to grab results across the platform. You can target specific media types without wading through junk.
News Monitoring
Keep tabs on what's breaking with dedicated tools. To check current headlines from Google, use google_news. For a Bing-based news roundup, you run bing_news. These tools pull localized stories and the most recent articles so your agent always has fresh info.
Advanced Functionality & Setup
Your client can also handle specialized tasks. You don't have to worry about setting up access; just use get_account_info to pull specific account details associated with the SERPHouse service directly into your workflow. When you need a general search on Google, google_search supports advanced parameters like location or language codes, giving you granular control over where and how the data comes from.
This setup lets you build complex information gathering routines in one conversation. You can run google_shopping to check product pricing, then immediately follow up with google_scholar for the academic background on that product's technology, all while verifying location scope using list_locations. The agent handles the complexity of these disparate data sources, giving you clean, usable results every time.
You get live access to everything Google and Bing show—no more relying on old training data.
How SERPHouse MCP Works
- 1 Your AI client sends a request to the SERPHouse MCP Server, specifying the target data (e.g., 'Find recent articles on X').
- 2 The server routes the query through the appropriate tool (like
google_scholarorbing_search), managing proxy rotation and bypassing anti-bot defenses. - 3 Your agent receives structured, live search results—including pricing metadata or article links—ready for analysis.
The bottom line is that your AI client doesn't need to know how to scrape; it just asks, and the server pulls the live data.
Who Is SERPHouse MCP For?
This is for the market researcher who needs real-time competitive pricing across multiple sources. It’s for the SEO specialist tracking local keyword performance month by month. Use this if your job requires gathering structured, live data from external search engines—not just general information.
Uses google_shopping and bing_search to track competitor product pricing across different geographic regions.
Runs targeted queries using google_search and checks local market performance via list_locations.
Aggregates structured data from disparate sources, running google_scholar alongside general searches to build a full knowledge graph.
What Changes When You Connect
- Live Data, Not Stale Knowledge: Forget relying on what your AI client was trained on. By using
google_searchorbing_search, you pull real-time SERP metadata, crucial for anything involving current pricing or breaking news. - Cross-Platform Coverage: You don't have to switch tools for different data types. Need images? Use
google_images. Want academic citations? Rungoogle_scholar. It keeps all your sources in one workflow. - Structured E-commerce Data: The
google_shoppingtool handles the complex task of extracting product name, price, and retailer directly from search results, saving you hours of manual data cleanup. - Location Specificity: Need to know what's trending only in Berlin? Use
list_locationsfirst, then run a geo-targeted query withgoogle_searchfor precise local insights. - Bypass Anti-Bot Measures: Because the integration handles proxy rotation and rate limits, your agent can execute deep scraping jobs without triggering Captcha blocks. It's built for volume.
Real-World Use Cases
Tracking Competitor Price Drops
A retailer needs to know if a competitor changed their price after lunch. They tell the agent: 'Check major electronics retailers in New York.' The agent runs google_shopping, pulling structured data from multiple sites, and summarizes any detected changes.
Academic Literature Review
A scientist is starting a new paper. They prompt the agent: 'Find recent papers on quantum computing.' The agent executes google_scholar to pull abstracts, identifies key authors, and lists citation counts from top journals.
Monitoring Local Market Shifts
A business owner wants to know about local economic changes. They ask the agent: 'What's the latest on shipping regulations in Miami?' The agent uses bing_news with a location tag, summarizing top headlines from regional sources.
Comprehensive Content Audit
An SEO manager needs to audit coverage across all media. They prompt: 'Run searches for our product in images, videos, and general search.' The agent cycles through google_images, google_videos, and google_search to build a complete content map.
The Tradeoffs
Using General Search for Shopping Data
Telling the agent, 'Find me a new laptop from Best Buy.' The agent just uses google_search and returns a mix of ads, articles, and links. You still have to click around.
→
Don't rely on general results. Always use the dedicated tool: run google_shopping. This forces the server to extract structured product data, giving you price and retailer info immediately.
Assuming Location Awareness
Prompting the agent: 'What's trending in London?' The server might default to a general US location if you haven't specified it first.
→
Always check list_locations first. Then, explicitly pass the correct location ID or parameter into your query using google_search or bing_news. Don't assume context.
Trying to Scrape Complex Sites
Asking the agent to scrape a private university database that requires a specific login workflow. The server can't handle custom, multi-step logins.
→
Stick to public search endpoints. Use google_scholar for academic content or run general searches via bing_search. This tool is designed around major platform APIs.
When It Fits, When It Doesn't
Use SERPHouse if your data need comes from the live, volatile output of Google or Bing—think pricing feeds, breaking news, or real-time image results. It's necessary when you need multiple types of search (e.g., images AND shopping) in one go.
Don't use this if all you need is data from a single, stable source like your internal CRM or an established database API. For those cases, connect to the specific service's native SDK instead. If you only need general text search and don't care about structure, a basic web scraper might work—but SERPHouse gives you reliable, structured results every time.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Serphouse API. 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 11 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manual market research is a nightmare of tabs and copy-pasting.
Today, if you need to compare product pricing or track local news trends, your process looks like this: You open Google Shopping in one tab, Bing News in another, Scholar in a third. Then, you manually click through dozens of search results, copy the URL, write down the price point, and paste it all into a massive spreadsheet. It's slow, and frankly, you lose data.
With SERPHouse MCP, that whole process collapses to one prompt. You ask your agent: 'Compare these three products in London.' The server simultaneously runs `google_shopping`, checks local news via `bing_news`, and gives you a clean summary of both the pricing *and* the regional context. It's instant.
SERPHouse MCP Server: Pull live search data into your workflow.
Before, if you needed to check scholarly sources and also see what people were talking about in the news, you had to run two completely different systems and then cross-reference them manually. You'd get a PDF from Scholar and a link from News—two separate workflows.
Now, your agent handles both tasks within one command. It pulls academic depth via `google_scholar` *and* current context via `google_news`. The data streams together for immediate analysis. That’s the difference.
Common Questions About SERPHouse MCP
How do I get location-specific results using google_search? +
You must first run list_locations to find the correct location code, then pass that ID or parameter into your query. This ensures Google knows which market you're targeting.
Can I use google_shopping for anything other than electronics? +
Yes. The tool is designed to scrape general product listings across various categories, allowing you to track items from apparel to industrial equipment using structured data.
If I search for images, do I need google_images or bing_images? +
Use the respective tool based on where you want the results. google_images directs the query to Google's image index, while bing_images uses Bing's collection.
Is google_scholar better than google_search for research? +
Yes. While google_search is general web results, google_scholar focuses only on academic journals and scholarly articles, giving you much more reliable source material.
How do I handle rate limits with bing_news or google_news? +
The SERPHouse integration manages proxy rotation and throttling automatically. It handles the request load so your agent doesn't hit provider quotas.
How do I check my current usage limits or quota using the `get_account_info` tool? +
You run get_account_info to retrieve your service status and remaining query capacity. The output tells you exactly how many searches you've used against your account, so you can plan larger data extraction jobs without hitting an unexpected wall.
What specific product metrics or structured data points does `google_shopping` return? +
It returns highly structured records including current titles, multiple vendor prices, and availability flags. This means you get consistent pricing data across different merchants in one clean JSON object, not just a list of links.
How should I specify language or geographic parameters for `google_search`? +
You pass the required location and language codes directly into the search function arguments. This lets your agent target specific linguistic markets (like French in Quebec) without needing to switch tools or endpoints.
How does SERPHouse handle the rotation of IP proxies and blocks? +
The SERPHouse API backend completely abstracts the proxy-network infrastructure. Your agent simply supplies queries (e.g., google_search), and the API intercepts these actions seamlessly, bouncing globally using elite endpoints to avoid Captchas.
Is the location fetching dynamic? How do I target a specific country search? +
Yes. Instruct your AI to retrieve overarching ID bounds leveraging the list_locations directory by passing a string fragment. The obtained taxonomy parameter guarantees local precision on your following google_search attempts.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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