Apify MCP for AI. Run scrapers, analyze data, manage web extraction.
Works with every AI agent you already use
…and any MCP-compatible client








How this MCP server connects to your AI agent
Apify connects your AI agent directly to large-scale web scraping and automation tools. It lets you run complex scrapers, manage vast datasets, and track execution histories—all through natural conversation.
Use this MCP to extract structured data from websites without writing a single line of code.
What AI agents can do with Apify Automation
Get dataset results
Retrieves the actual structured records (items) stored within a specific dataset.
Get run details
Fetches comprehensive information and logs about one particular actor execution run.
List actors
Lists all the available web scraping actors configured in your Apify account.
You can trigger specific actors to scrape websites or run automated processes on demand.
The agent fetches raw data records from your datasets, allowing you to process and analyze the collected information instantly.
You get real-time visibility into past actor executions, checking run history and failure details for reliability checks.
The MCP lets you list all available actors, datasets, and configured tasks to manage your entire data inventory.
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What AI agents can do with Apify: 7 Tools for Web Automation
These tools give your agent specific commands to list assets, run scrapers, check status, and retrieve structured data from web sources.
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 Apify on VinkiusGet Dataset Results
Retrieves the actual structured records (items) stored within a specific dataset.
Get Run Details
Fetches comprehensive information and logs about one particular actor execution run.
List Actors
Lists all the available web scraping actors configured in your Apify account.
List Datasets
Retrieves a list of every dataset you have collected data into.
List Actor Runs
Lists the history and status of your most recent actor executions to check...
List Actor Tasks
Shows you all the saved, configured tasks associated with your actors for reuse.
Run Actor
Starts a new instance of an actor run using specified inputs and configurations.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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 Apify, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ 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 Apify. 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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Built on the Model Context Protocol (MCP) for 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 connection provides 7 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually gathering market intelligence takes way too long., Solved with Vinkius AI Gateway
Today, pulling comprehensive web data means bouncing between multiple scraping tools. You have to manually check if the script ran, then go into the dataset view just to copy a few key figures. It's endless clicking through status dashboards and API documentation.
With this MCP, you tell your agent what information you need—like product prices or contact lists. The system triggers the necessary actors, waits for the data collection to finish, and then presents the clean, structured records directly in the conversation. You get actionable results without touching a dashboard.
Apify MCP Gives You Full Control Over Your Web Data Pipeline
You stop manually triggering runs and checking status updates. Instead, you use the `run_actor` tool to start the process and then rely on conversational prompts to monitor progress or retrieve the final output via `get_dataset_results`.
It's not just about getting data; it's about managing the whole flow. You get a single point of control for execution, monitoring, and retrieval.
What your AI can actually do with this
Need to pull information off the web in bulk? This connector gives your agent complete control over large-scale data extraction projects. You can list available scrapers, run them with specific inputs, and watch them work right from your chat window. Once the scraping finishes, you don't just get a link; you retrieve the actual structured records—the items—from massive datasets so your AI client can analyze them immediately.
Plus, you can check the status of recent runs and view detailed logs to debug anything that breaks in your automation flow. Connecting this MCP through Vinkius means you manage all your scraping needs from one single connection point with any compatible agent.
019dd0b9-d298-70b6-9f35-5e49791139d9 Here's how it actually works
The bottom line is you tell your agent what kind of web data you need, and it handles the complex execution and retrieval process for you.
Subscribe to this MCP on Vinkius and provide your Apify API Token.
Your agent uses the token to query available scrapers, datasets, or run status for validation.
You issue a conversational command—for example, 'Run Scraper X with input Y'—and receive structured data results directly in the chat.
Who is this actually for?
This MCP is essential for Data Scientists, Research Analysts, and Automation Engineers who regularly deal with unstructured or high-volume public web data. If your job involves gathering market intelligence or building automated pipelines that scrape websites, this tool saves you hours of API juggling.
You use the MCP to list datasets and retrieve results from specific runs, allowing your agent to immediately analyze trends in scraped market data or research papers.
You trigger actor runs and check run details directly through your agent. This lets you monitor the health of complex scrapers without switching between dashboards.
You use this MCP to get a quick view of automated data collection tasks, ensuring that market intelligence feeds are running and retrieving fresh results.
What Changes When You Connect
Get immediate access to structured data. Instead of navigating a dashboard just to pull results, you use the get_dataset_results tool to have your agent fetch the records instantly for analysis.
Maintain full visibility on automation health. You don't guess if your scraper ran; using list_actor_runs gives you a clear history and status check for every execution.
Debug complex pipelines easily. When something goes wrong, querying the run details via get_run_details provides the logs needed to pinpoint exactly where the script failed.
Manage all your scrapers centrally. The ability to use list_actors means you never lose track of which scraping tools are available or need updating.
Save time configuring tasks. Instead of setting up the same scraper parameters repeatedly, listing and reusing configured settings with list_actor_tasks saves setup time.
See it in action
Tracking Competitor Pricing Changes
A market researcher needs to know if a competitor changed its pricing structure this week. They ask their agent, which uses run_actor, to scrape the target site multiple times. Then, using get_dataset_results and having the AI analyze the output, they get a summary report of all price changes in one go.
Compiling Industry Trend Reports
A data scientist needs to gather 500 profiles from LinkedIn. They first use list_actors to find the right scraper, then trigger it with specific parameters using run_actor. Finally, they fetch all records via get_dataset_results and ask their agent to summarize key industry terms.
Auditing Data Collection Failures
An automation engineer notices a data feed seems incomplete. They use list_actor_runs to check the last 10 runs, find an execution marked 'failed', and then call get_run_details to diagnose if it was an API limit or a site structure change.
Building Data Pipelines from Scratch
A product manager needs data on 10 different types of product reviews. They ask the agent to list all relevant datasets using list_datasets. The AI then retrieves results for each dataset, allowing them to compare and synthesize findings across multiple sources.
The honest tradeoffs
Trying to scrape data manually
Copying URLs from search engine results page by search engine results page. This is slow and misses structured metadata.
Use list_actors to find a dedicated scraper, then use run_actor with specific inputs. The agent handles the repetitive collection process for you.
Ignoring run history
Assuming data is fresh because it was collected last week. You risk basing decisions on stale or incomplete information.
Always use list_actor_runs to confirm the date and status of your data source, ensuring you pull from a successful, recent execution.
Overloading the agent with logs
Asking for every single log detail when all you needed was the final count of records. This generates noise.
First, use list_datasets to confirm the dataset exists. Then, request only a summary or specific fields using get_dataset_results, avoiding unnecessary debugging logs.
When It Fits, When It Doesn't
Use this MCP if your primary need is extracting high-volume, structured data from dynamic websites or complex web sources. This tool isn't for simple lookups; it manages entire pipelines—from triggering the scraper to retrieving the final records. Don't use it if you just need to check a single API endpoint (use a standard API connector instead). If your requirement is purely internal, database-to-database sync, this MCP won't help because it focuses on external web sources. However, if your pain point involves 'I have data scattered across 20 different websites and I need them all in one place,' then this MCP is exactly what you need.
Questions you might have
How does Apify MCP help me manage my scrapers? +
You use list_actors to see all your available scraping scripts. You can also check saved configurations using list_actor_tasks, so you never lose track of a specific scraper setup.
What is the difference between `run_actor` and `get_dataset_results`? +
run_actor executes the scraping process, generating raw data. You then use get_dataset_results to fetch the actual structured items that were successfully collected into a dataset.
Can I see if my scraper ran successfully last week? +
Yes. Use list_actor_runs. This tool shows you the history of executions, allowing you to check status and identify specific run IDs for deeper investigation using get_run_details.
Does Apify MCP handle large amounts of data? +
Yes. The entire system is built for scale. You use the MCP to manage datasets, which are designed specifically to hold massive collections of structured web data.
If I need to debug a broken scraper, what should I use in Apify MCP? +
You start by using get_run_details on the failing run ID. This retrieves detailed metadata and logs that tell you exactly why the automation failed.
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