Apify MCP for AI. Direct web data extraction via conversation.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Apify MCP connects your AI agent directly to a full-stack web scraping platform. You can list available scrapers, run bots asynchronously or synchronously, and pull structured data records in raw JSON format, all through natural conversation.
What your AI can do
Abort run
Stops a running Apify scraper job immediately if the scrape is going off track or if enough data has already been collected.
Get account limits
Checks your current consumption and subscription limits to make sure you don't hit an overage charge.
Get dataset items
Exports the structured JSON data from a completed Apify dataset, supporting large bulk downloads by page limit.
Lists every scraper bot (Actor) configured in your Apify account so you know what data is accessible.
Starts a web scrape, either waiting for it to finish immediately or running it in the background for long-term monitoring.
Retrieves the full dataset of scraped records as JSON objects after a job completes.
Allows you to stop runaway scrapes or tell an active scraper to crawl new URLs it finds.
Gives you a status report on your account's usage, including compute unit consumption and proxy bandwidth.
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Apify: 10 Tools for Web Data Operations
These tools give your agent the power to monitor, control, and retrieve data from every stage of web scraping—from listing bots to downloading final datasets.
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 VinkiusAbort Run
Stops a running Apify scraper job immediately if the scrape is going off track or if enough data has already been collected.
Get Account Limits
Checks your current consumption and subscription limits to make sure you don't hit...
Get Dataset Items
Exports the structured JSON data from a completed Apify dataset, supporting large...
Get Key Value Store
Retrieves miscellaneous files related to a run, like screenshots or configuration...
Get Run
Checks the status and metadata of an active scrape job so you know if it's still...
List Actors
Shows all scraper bots available in your account, including their IDs and default settings for triggering a run.
List Webhooks
Lists the external systems that get notified when an actor run succeeds or fails.
Push To Queue
Tells a currently running scraper to add new URLs it discovers to its list of pages...
Run Actor Sync
Runs a short-lived scraper and makes your agent wait until it finishes before giving...
Run Actor
Starts an Apify scraper bot in the background using custom input settings, returning...
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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 Apify, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
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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 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually collecting web data is a grind.
Right now, if you need product listings from 50 different pages, you're sitting here clicking through tabs: copy the URL, paste it into your spreadsheet, manually adjust proxy settings, and then run the whole thing. It’s slow, prone to breakages, and frankly, exhausting.
With this MCP, that whole process disappears. You tell your agent what data fields you need, and the bot handles the entire traversal. The result? Structured JSON records appear instantly for review. That's a huge difference.
Apify MCP lets you manage web scraping runs.
Before this, if a scrape failed halfway through, your only option was to manually restart it and hope nothing broke. You had no easy way to tell the process, 'Hey, stop; we got enough data,' or 'Wait, crawl these 10 more specific links.'
Now you can control the entire lifecycle: start with `run_actor`, monitor status with `get_run`, and then dynamically feed it new URLs using `push_to_queue`. You're in charge of the bot.
What your AI can actually do with this
This connector lets you direct complex data extraction workflows entirely through chat. Instead of setting up API keys and running scripts locally, your agent talks to the Apify system—it finds the right scraper bot, runs it, monitors its progress, and pulls the resulting structured datasets into your context window. You can even tell the running process to crawl new pages you discover mid-job.
Because Vinkius hosts this MCP in the catalog, you just connect once from any compatible client, giving you immediate access to run these sophisticated web tasks without touching a command line.
019d754f-450f-733b-ad5f-50479ed751b0 Here's how it actually works
The bottom line is that you treat complex web scraping like a conversation; the AI handles all the underlying API calls and state management for you.
First, connect your AI client to the Apify MCP and tell it which scraper bot (Actor) you need for the job.
Next, trigger the scrape. If it's a big job, run it asynchronously; if it’s quick, let the agent wait until it confirms completion.
Finally, ask your agent to pull the data. It will retrieve the structured records and inject them directly into the chat context for you to read or process.
Who is this actually for?
This is for data professionals who spend too much time manually building scrapers or running them in brittle scripts. If you're tired of checking dashboards to see if a job finished, this MCP saves you the headache.
Needs to trigger scheduled data extraction logic and map complex Apify objects within conversational queries.
Wants to tell their agent, 'Scrape product prices for these 50 URLs,' and get the resulting JSON compiled instantly into a markdown table.
Needs to augment an agent's real-time capabilities by feeding it massive structured site data scraped via headless browsers, without writing Python wrapper code.
What Changes When You Connect
You bypass writing boilerplate Python. Instead of calling run_actor and then polling with get_run, you just ask your agent to check the status, making the whole process conversational.
Data retrieval is simple: Once the scrape finishes, use get_dataset_items to pull all structured data directly into the chat context. It handles massive JSON exports for you.
You maintain control over expensive jobs. If a scraper starts going wild or runs past its usefulness, you can hit 'stop' using abort_run, saving compute units and time.
The system is resilient because you don't have to manually manage state. The agent tracks the job ID from run_actor and knows when to query for results using get_dataset_items.
It helps with governance too. Before you start anything huge, check get_account_limits. You don't want a runaway scrape wiping out your budget because you forgot about it.
See it in action
Monitoring competitor pricing changes
A market researcher needs to track product prices daily. They use list_actors to find the correct price scraper, then use run_actor for a scheduled job. When the results arrive, they pass them through the agent and ask it to format the latest data into a summary table.
Crawling deeply linked product catalogs
An AI developer needs to scrape an entire website section that involves clicking 'next page' buttons. They use run_actor and then follow up by calling push_to_queue, telling the running process exactly which newly found URLs it must crawl.
Checking for data completeness
A data engineer runs a scrape and is worried about missing metadata. They use get_key_value_store to pull down any attached screenshots or configuration files from the job, ensuring they have all the necessary audit details.
Verifying service health after failure
A client runs a large scrape and it fails. They use get_run first to see the exact error status, then check list_webhooks to confirm if external systems were supposed to get notified about the failure.
The honest tradeoffs
Treating data as a single API call
Trying to pull all data by just calling get_dataset_items without thinking about pagination. You might get an error or, worse, only get the first 100 records.
Always check your dataset needs and use the limit/offset parameters in get_dataset_items. If you need a massive pull, run it for a while, then use list_actors to confirm the correct bot is running.
Starting big jobs without checking limits
Launching an intensive scraper that consumes 50 Compute Units, only to find out later via billing that you were already at 90% capacity. Wasted cycles.
Always start with get_account_limits. Check your compute unit usage and proxy bandwidth before launching any major scrape using run_actor.
Ignoring the job status
Triggering a complex multi-page crawl using run_actor and then assuming the data is ready immediately, leading to an empty dataset return.
Immediately follow up the run_actor call with get_run. Use this tool repeatedly until the status shows 'SUCCEEDED' before attempting to retrieve data via get_dataset_items.
When It Fits, When It Doesn't
Use this MCP if your goal is extracting structured web content from diverse sources and you need full control over the scraping lifecycle. It’s perfect for iterative, complex tasks (like crawling a site using push_to_queue) that can't be solved by simple API calls.
Don't use it if: 1) You are only retrieving data from a known source database (use a dedicated database connector). 2) Your web scraping logic is extremely complex and requires custom, non-standard browser interactions (you might need to write the wrapper yourself).
If you just need to list available scrapers, use list_actors. If you only care about checking if your budget allows another scrape, stick with get_account_limits. The power of this MCP is in managing the entire state—from initiation to retrieval and cleanup.
Questions you might have
How do I list all available scrapers using Apify MCP? +
You use the list_actors tool. This shows you every scraper bot (Actor) you have access to, giving you their IDs so you know exactly what job they're built for.
What is the difference between run_actor and run_actor_sync? +
The difference is timing. run_actor starts a background process, which is best for long jobs because it returns immediately. run_actor_sync blocks your agent until the job finishes; use this only for very short tasks (under five minutes).
How do I get the final data from an Apify run? +
After a scrape is complete, use get_dataset_items. This pulls all the structured records and provides them to your agent in usable JSON format.
Can I stop a running scrape with abort_run? +
Yep. You can call abort_run anytime you need to halt a job, which is critical if the scraper starts pulling junk data or exceeds your budget.
How do I check my compute unit usage using get_account_limits? +
It immediately reports your current consumption against your subscription cap. This tool monitors both compute units and proxy bandwidth, helping you prevent unexpected overage charges on large scraping jobs.
What information does get_run provide about an active scraping job? +
The endpoint provides the run's current status, metadata, and consumption details. You can poll this tool to track if a long-running scrape is still running or has successfully completed.
When should I use get_key_value_store instead of getting dataset items? +
Use it for non-structured files like screenshots, configuration inputs, or raw HTML snapshots. The key-value store holds arbitrary binary and text data linked to a specific run ID.
How does list_webhooks help with automated workflows? +
This tool lists all configured webhooks, which enable external systems to react when an actor run succeeds or fails. It is essential for building reliable, event-driven architectures.
How can the AI agent run a scrape on a list of product URLs? +
First, find your specific scraping Actor ID via list_actors. Then, prompt your agent to execute run_actor, providing the target URLs formatted as a structured JSON input payload. It returns a 'Run ID'. You can poll this run via get_run, and once it succeeds, the agent calls get_dataset_items to pull all acquired data straight to your window.
Can the agent interact with run configurations mid-way during crawling? +
Yes. If an Apify crawler is currently executing and utilizes a Request Queue, you can instruct your agent to call push_to_queue. Doing so dynamically ships new URLs to the active queue instance, extending the current web crawl without needing to stop or restart the Actor.
Can my AI automatically detect scraping timeouts and debug the failure? +
Absolutely. Because your agent can track real execution flows with get_run, it's aware if it transitions to TIMED-OUT or FAILED states. Subsequently, you can ask the agent to examine the KV Store log outputs ensuring the underlying issue (e.g. captcha block, blocking proxy) is identified immediately.
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