Apify MCP for AI. Run and structure large-scale web data extraction.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Apify manages web scraping actors, collecting structured data at scale. This MCP lets your agent list available scrapers, trigger runs with custom inputs, and monitor execution health.
You get access to all resulting datasets—you can then fetch specific records for immediate analysis or check the full history of any single run.
What your AI can do
Get dataset results
Fetches the actual record items from an existing dataset after a successful scrape.
Get run details
Retrieves specific metadata and logs for one particular scraping execution run.
List actors
Retrieves a list of every available scraper actor in your Apify account.
List every configured scraper actor in your account so you know what data sources exist.
Start a new extraction run for an actor, passing custom inputs like hashtags or URLs to customize the scrape.
Fetch specific data items from a completed dataset so you can analyze them immediately.
View the detailed log or status of any past actor execution to debug failures or check completion times.
List configured task presets, letting you reuse complex scraping inputs without re-entering them.
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Apify: 7 Tools for Data Extraction
These seven tools let your agent handle the entire process, from listing available scrapers to getting final data record sets.
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
Fetches the actual record items from an existing dataset after a successful scrape.
Get Run Details
Retrieves specific metadata and logs for one particular scraping execution run.
List Actors
Retrieves a list of every available scraper actor in your Apify account.
List Datasets
Shows all the datasets you have collected data into within your account.
List Actor Runs
Lists the status, history, and identifiers of recent scraper executions.
List Actor Tasks
Shows all saved task configurations so you can reuse complex inputs for new runs.
Run Actor
Initiates a new web scraping run for a specific actor using defined inputs.
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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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 connection provides 7 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The current data extraction process involves constant context switching.
Right now, you run a scraper in one tab. You copy key identifiers into a spreadsheet, open another tool to manage those IDs, then go back and manually paste the list of URLs for the next run. This tedious cycle of copying, pasting, monitoring dashboard tabs, and switching between different services eats up hours every week.
With this MCP, you automate that entire sequence. You tell your agent exactly what needs scraping and where to put it. The process moves from a confusing web of clicks into one clean conversational flow—you just get the data.
Getting Clean Data with `get_dataset_results`
The manual step that goes away is the tedious process of downloading a raw CSV file, opening it in Excel, and then manually cleaning out junk columns or merging data from different sheets. You don't have to touch a spreadsheet.
Your agent gets you the clean records directly; they pop into your conversation window formatted for immediate use by your workflow. Period.
What your AI can actually do with this
Managing web scraping used to mean juggling dozens of tabs, running complicated scripts locally, and praying your data structure remained consistent. Now, you just tell your agent what needs collecting. This MCP connects your entire workflow to Apify's platform, letting you manage the whole process through natural conversation.
Need to scrape a list of product reviews? Your agent can first check which scrapers are available and then trigger a run with specific parameters. Once that job finishes, instead of hunting for files, you simply ask your agent to retrieve the resulting data from the dataset. You get clean records—profile URLs, captions, engagement counts—ready for immediate analysis.
If something goes wrong, you don't have to start over; you can query the run history and debug exactly where it failed. This centralized control makes managing large-scale web automation simple, all managed through Vinkius.
019dd0b9-d298-70b6-9f35-5e49791139d9 Here's how it actually works
The bottom line is that your agent handles the entire lifecycle: setup, execution, and retrieval—all within a single conversation.
Subscribe to this MCP and provide your Apify API Token.
Use the agent to find or list a specific actor (scraper) you want to use.
Tell the agent to trigger the run, providing any necessary inputs. After completion, ask it to retrieve the final structured data from the resulting dataset.
Who is this actually for?
Data Science teams who spend too long manually cleaning scraped data. Product Managers needing quick insights into market trends. Or any ops engineer tired of clicking through multiple dashboards just to monitor one scraping job.
Uses this MCP to run targeted scrapes on niche websites, then uses the dataset retrieval tools to pull structured results for immediate trend analysis.
Triggers and monitors multiple actor runs sequentially, using list_actor_runs and get_run_details to build reliable, scheduled data pipelines.
Gets an instant view of automated data collection by listing datasets and summarizing what was collected about a specific market or competitor.
What Changes When You Connect
Skip the setup time. Instead of manually entering scraping parameters every time, you can use list_actor_tasks to pull saved configurations and reuse them for new jobs.
Stop guessing if a scrape worked. By checking run history with list_actor_runs, you instantly see the status (success or failure) without diving into complex dashboards.
Analyze data immediately. Using get_dataset_results lets your agent pull clean records, which you can then ask it to summarize or categorize right away.
Isolate problems quickly. If a scrape fails, get_run_details gives you the exact logs and metadata needed to pinpoint whether the failure was due to an input issue or an external website change.
Maintain visibility. You don't need to remember which data lives where; simply use list_datasets to get a clean overview of all your collected information.
See it in action
Tracking competitor product changes
A marketing analyst needs to monitor price changes for 50 items daily. They first use list_actors to find the correct scraper, then run it with a list of URLs using run_actor. Finally, they ask the agent to pull all the updated prices from get_dataset_results, giving them an immediate comparison report.
Debugging unreliable data collection
An engineering team runs a scraper but gets incomplete records. They use list_actor_runs to find the run ID, then call get_run_details. The detailed logs reveal the failure point was due to missing required headers, allowing them to fix the actor configuration immediately.
Building a repeatable research workflow
A researcher needs to scrape data for three different geographical areas. Instead of re-entering location parameters each time, they use list_actor_tasks to pull the saved templates and then run the actor three times with minimal manual input.
Quickly assessing dataset scope
A product manager just finished a large scrape and needs to know how much data was collected. They use list_datasets first, then tell the agent to run get_dataset_results on that specific dataset ID to confirm record counts and field types.
The honest tradeoffs
Assuming data is ready
Asking for results from a dataset immediately after running the scraper, without waiting or checking status.
Always check first. After using run_actor, use list_actor_runs to get the run ID. Then, monitor it with get_run_details until the job shows as complete before attempting to call get_dataset_results.
Forgetting saved inputs
Manually re-entering complex scrape parameters (like XPath selectors or multi-page ranges) for a recurring project.
Use list_actor_tasks to check your saved settings. Then, when you run the job using run_actor, select the pre-configured task name instead of providing raw inputs.
Confusing assets with runs
Trying to analyze data by simply listing all available scrapers without knowing which ones have been successfully executed.
To see what actually ran, use list_actor_runs. This shows the history and status of execution. If you want to know where the results are stored, check list_datasets.
When It Fits, When It Doesn't
Use this MCP if your core problem is managing complex, multi-step data collection from dynamic web content (e.g., scraping e-commerce sites or social media feeds). The seven tools cover the entire lifecycle: finding assets (list_actors, list_datasets), initiating work (run_actor), and retrieving/debugging results (get_dataset_results, get_run_details). Don't use this if you just need to read a simple JSON file or interact with a single, stable API endpoint. For those cases, a direct API connector is cleaner. This MCP is for the full automation pipeline—the kind where success depends on monitoring and structured retrieval.
Questions you might have
How do I see what scrapers are available using list_actors? +
You call list_actors to get a full roster of every scraper you've set up. This tells you which data sources your agent can access for scraping.
What is the difference between list_datasets and list_actors? +
This matters: list_actors shows the tools (the scrapers) you use to collect data. list_datasets shows where the collected results are stored after a scrape completes.
How can I check if my scraper run actually finished? +
You need to use list_actor_runs. This tool gives you the history and status of every job, letting you confirm when it's safe to pull data.
I want to analyze data from a failed scrape. Do I use get_run_details? +
Yes, get_run_details is the right tool. It pulls specific logs and metadata for that run, telling you why it failed, which is more useful than just seeing 'failed' in a list.
Can I reuse scrape settings? +
Totally. Use list_actor_tasks to view saved inputs and then pass those task names when calling run_actor, saving you from re-entering parameters.
When I use `get_dataset_results`, how do I filter for specific record types or dates? +
You pass filters directly with your request. You specify criteria—like date ranges or field values—in the query parameters. This keeps your result set clean and focused only on the data you need.
When I use `run_actor`, what format must my input data be in? +
You must provide inputs as structured JSON objects that match the actor's required schema. Passing simple text usually fails; proper JSON ensures the scraper gets exactly the parameters it needs.
Can I use `list_actor_runs` to check an actor’s historical performance or reliability? +
Yes, you can query run summaries and status codes. This lets you track average completion time or spot patterns of slow execution across many runs. It's useful for planning capacity.
Can I provide input parameters when running an actor? +
Yes! Use the run_actor tool and provide the optional input JSON object to configure specific scraper settings for that run.
How do I see the items collected in a dataset? +
Run the get_dataset_results query with your Dataset ID. The agent will retrieve the data records, which you can then ask the AI to summarize or analyze.
Is it possible to check the status of a specific actor run? +
Absolutely. Use the get_run_details tool and provide the Run ID. Your agent will retrieve the status (RUNNING, SUCCEEDED, FAILED) and metadata for that specific execution.
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