Olostep MCP. Automate massive data extraction from live websites.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Olostep handles large-scale web scraping using a headless browser API that renders JavaScript and returns structured data. You run complex, automated data extraction workflows through natural conversation with your AI client.
It manages everything from single URL scrapes to full batch orchestration.
What your AI agents can do
Check olostep status
Verifies your API connection to Olostep and confirms connectivity status.
Create agent
Initializes a new scraping agent profile within the system.
Create batch
Starts a bulk scrape job, taking multiple URLs separated by commas as input.
Verify the connection between your AI client and the Olostep service using check_olostep_status.
List, retrieve details, or create new scraping agents using list_agents, get_agent, and create_agent.
Initiate a large-scale scrape job by passing multiple URLs as comma-separated values to create_batch.
Get the status and details of existing scraping jobs using get_batch or retrieve the final structured data via get_batch_results.
Perform a quick scrape on one web page, specifying the output format (markdown, html, or text) with scrape_url.
Check your current usage statistics—pages scraped and bandwidth used—using the get_usage tool.
Ask AI about this MCP
Supported MCP Clients
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Olostep MCP Server: 10 Tools for Web Scraping Operations
Use these ten tools to manage every step of your web scraping workflow—from listing agents to retrieving final batch results.
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 Olostep on Vinkius019dd130check olostep status
Verifies your API connection to Olostep and confirms connectivity status.
019dd130create agent
Initializes a new scraping agent profile within the system.
019dd130create batch
Starts a bulk scrape job, taking multiple URLs separated by commas as input.
019dd130get agent
Retrieves the full details and configuration for a specific scraping agent.
019dd130get batch results
Fetches the final structured results (Markdown/JSON) once a scraping batch is finished.
019dd130get batch
Checks the operational status, job ID, and progress of a running or completed scrape batch.
019dd130get usage
Provides current metrics on API usage, including pages scraped and bandwidth consumed this cycle.
019dd130list agents
Lists all available scraping agents configured under your account.
019dd130list batches
Provides a list of every scrape batch job you've run, including IDs and status.
019dd130scrape url
Scrapes the content from a single web page; lets you specify if you want markdown, html, or plain text output.
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 Olostep, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ 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 Olostep. 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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
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.
Copying links, scraping manually—it slows down research.
Today, if you need data from multiple sources, your workflow looks like this: open the first page, copy the necessary text; open the second tab, repeat the process. You end up with a Frankenstein document full of inconsistencies and manual effort. It's slow, error-prone work.
With Olostep MCP Server, you tell your agent to run a batch scrape using `create_batch`. The system handles the orchestration: rendering JavaScript, pulling clean data, and giving you structured results via `get_batch_results`—all from one chat prompt.
Olostep MCP Server: Get Structured Data From Live Websites
Manual data collection means checking every single URL individually, which is impossible for large campaigns. You also have to worry about whether the site's JavaScript will mess up your scraped content.
Olostep solves this by accepting bulk URLs and running them through a powerful headless browser engine. You get perfect, structured JSON or Markdown output, allowing you to feed it directly into another process.
What you can do with this MCP connector
Olostep MCP Server - Web Scraping & Data Extraction
This server lets you run huge, automated data scrapes through your AI client. It's built for complex workflows that need JavaScript rendered and structured output—not just basic HTML grabs. You handle everything from checking connectivity to running massive job batches.
You start by verifying the link between your agent and Olostep using check_olostep_status. This confirms you’re connected and ready to go.
You manage scraping agents with three tools: you can see every configured profile by calling list_agents, pull all the specific details for one agent using get_agent, or initialize a brand new agent setup through create_agent.
For quick data pulls, you use scrape_url. This scrapes content from just one web page and lets you specify exactly what format you need: markdown, plain text, or full HTML. If you're running a massive job, you initiate it by calling create_batch, passing multiple URLs separated by commas to start the bulk scrape.
The system tracks everything you do. You can see every scrape batch job you’ve ever run—their IDs and status—by using list_batches. To check on a specific running or finished job, you use get_batch to get its operational status and job ID. When the scraping is done, you pull the final structured results (Markdown or JSON) using get_batch_results.
To keep tabs on your usage, always run get_usage. This gives you current numbers on how many pages you’ve scraped and how much bandwidth you've used this cycle.
019dd130-c17c-722b-9dfb-eaa8dd44c537 
How Olostep MCP Works
- 1 First, connect your AI client to the Olostep MCP Server using your API Key.
- 2 Next, tell your agent what data you need: 'Create a batch scrape for these 10 URLs' or 'Scrape this single page in markdown format.'
- 3 The server processes the request (rendering JavaScript if needed) and returns structured results—either immediate output from
scrape_urlor a job ID to monitor withget_batch.
The bottom line is, you talk to your agent like a person, and it handles all the complex API calls needed to get clean data from the web.
Who Is Olostep MCP For?
Data Engineers who need reliable batch processing without writing boilerplate code. Researchers stuck copying text snippets into Notion. Developers building RAG pipelines who need high-speed, structured source content.
Uses scrape_url to quickly grab metadata from competitor product pages or uses list_agents to manage multiple automated data feeds.
Runs large-scale campaigns using create_batch, then checks the final output and metrics with get_usage to ensure compliance.
Feeds raw web content into a structured pipeline by scraping pages, ensuring the resulting data is clean enough for immediate use in documentation or articles.
What Changes When You Connect
- Scale data acquisition instantly. Instead of manually scraping pages, running
create_batchlets you manage hundreds of URLs in a single job. - Get clean, structured output every time. Olostep uses a headless browser that renders JavaScript, so the content is accurate—not just raw HTML garbage.
- Keep track of everything with zero clicks. Use
list_batchesandget_batchto see real-time status updates for all your scraping jobs. - Monitor cost and limits easily. The
get_usagetool shows you exactly how many pages you've scraped and the bandwidth used, keeping you accountable. - Control your data sources with agents. You can use
create_agentto build specific scraping profiles tailored for different sites or domains.
Real-World Use Cases
Tracking Competitor Pricing
A product manager needs to track pricing changes across 50 competitor pages. Instead of writing a script, they ask their agent: 'Create a batch scrape for these 50 URLs.' The agent uses create_batch, and when done, the PM checks get_batch_results to get all the structured data ready for analysis.
Building a Knowledge Base from Articles
A researcher needs content summaries from 10 different scientific journals. The agent runs a targeted scrape using scrape_url, specifying 'markdown' format. This gives the researcher clean, article-ready text to feed into their RAG pipeline.
Auditing API Costs
An operations engineer wants to know if their scraping volume is too high before running a major campaign. They simply call get_usage. This immediately shows the total pages scraped and remaining bandwidth, preventing unexpected overages.
Managing Scraping Efforts
A development team needs to ensure all their scraping jobs are accounted for. They use list_agents to see what agents are active, then run list_batches to get a complete history of every job ID.
The Tradeoffs
Treating the server like a simple GET request
The user tries to scrape multiple pages by listing them in one long, comma-separated string without using the proper batch function. They might just use scrape_url repeatedly.
→
Don't call scrape_url for batches of pages. Instead, pass all target URLs to create_batch. This ensures job orchestration and status tracking are handled correctly through get_batch.
Ignoring data format options
The user asks the agent to scrape a page but forgets to specify if they need raw HTML or clean text, resulting in messy, unusable output.
→
Always use scrape_url and explicitly state your desired output format: markdown, html, or plain text. This guarantees the structured data you get back is ready for your next step.
Assuming immediate results
The user runs a massive batch job using create_batch and immediately expects the final results from get_batch_results, which will fail because the process hasn't finished.
→
After calling create_batch, you must first monitor progress by running get_batch repeatedly until the status is 'Complete.' Only then should you call get_batch_results.
When It Fits, When It Doesn't
Use this server if your core need is gathering structured data from live, JavaScript-rendered websites at scale. If you're pulling content from a database or a file system, you don't need Olostep—you just use standard database connectors. Don't use it if you only need to scrape static PDFs or images; the tool handles web page rendering, not file formats. You should rely on create_batch for campaigns involving dozens of URLs and reserve scrape_url for one-off checks. Always check your limits first using get_usage; that's how you avoid hitting rate limits when running big jobs.
Common Questions About Olostep MCP
How do I scrape multiple pages with Olostep's `create_batch`? +
You provide the URLs as a comma-separated list. The agent handles the rest, running them all in one scheduled job and giving you job IDs to track.
What format does `get_batch_results` return? +
It returns structured data, typically Markdown or JSON, depending on what was requested during the batch creation. This makes it immediately usable for processing.
Can I check my API usage with Olostep's `get_usage` tool? +
Yes, calling get_usage gives you a clear breakdown of your current month's activity—total pages scraped and bandwidth consumed.
Is Olostep good for scraping JavaScript-heavy sites using `scrape_url`? +
Absolutely. The server uses a headless browser that renders JavaScript first, meaning the content you get back is what the live user sees, not just the initial HTML.
If I run into API connectivity issues, how do I use `check_olostep_status`? +
It verifies your direct connection to the Olostep service. Running this tool confirms that your AI client can communicate with the server endpoint before you initiate any large-scale scraping operations. This saves time debugging simple authentication failures.
How do I track my historical scrapes and manage multiple jobs using `list_batches`? +
It provides an overview of all your completed or running batches. You get the job ID, creation date, and current status for quick reference without needing to download individual result sets. This is great for auditing.
When I call `scrape_url`, how do I specify that the data should be in JSON format? +
You must pass the desired output format (markdown, html, or text) as a parameter to the tool. Specifying 'json' ensures the agent receives structured key-value pairs immediately, making it easy for your LLM pipeline to consume.
After setting up agents with `create_agent`, how do I view and manage my existing scraping resources? +
You use list_agents to retrieve a directory of all created agent instances. You can then select a specific agent ID and use the get_agent tool to pull detailed configurations for modification or monitoring.
How do I scrape a web page via AI? +
Use the scrape_url tool with the target URL and optional format (markdown, html, or text). The content is extracted and returned instantly.
Can I scrape multiple URLs at once? +
Yes. Use create_batch with comma-separated URLs to submit a batch job. Track progress with get_batch and retrieve results with get_batch_results.
What are scraping agents and how do I use them? +
Agents are reusable scraping configurations with custom extraction rules. Use create_agent to set one up and list_agents to manage them.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.