Apify MCP for AI. Audit your entire scraping stack from chat.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Apify MCP lets your agent audit and manage all cloud automation and web scraping jobs without logging into the external console.
You can list every active automation setup, check recent runs for success rates, or pull specific data items from any dataset—all through natural chat conversation.
It gives you real-time operational visibility across complex data pipelines.
What your AI can do
List webhooks
Lists configured webhooks that trigger actions when certain events occur.
Get actor
Gets detailed information for a single, specific automation setup (actor).
Get dataset items
Retrieves individual data records from an established dataset.
Lists every active web scraping or data pipeline setup configured in your account.
Checks the history of automated tasks, showing when they ran, if they succeeded, and what their current schedules are.
Pulls specific records or data points from your completed datasets for immediate review.
Gathers metadata about the foundational pieces of your system, like webhooks and key-value stores.
Ask an AI about this
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Apify Alternatives: 10 Tools for Automation Ops
These tools let your agent interact directly with every core component of your cloud automation system—from listing data to checking run history.
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 VinkiusList Webhooks
Lists configured webhooks that trigger actions when certain events occur.
Get Actor
Gets detailed information for a single, specific automation setup (actor).
Get Dataset Items
Retrieves individual data records from an established dataset.
Get User Info
Provides basic details about the user connected to the system.
List Actors
Lists all existing automation setups (actors) in your account.
List Datasets
Lists all saved datasets containing scraped or generated data.
List Key Value Stores
Retrieves a list of key-value storage containers used by your automation jobs.
List Runs
Provides a history of execution runs for any specific actor or task.
List Schedules
Lists the automated timing schedules configured to run your tasks periodically.
List Tasks
Shows all defined, granular tasks within a specific automation setup.
Security and governance baked right in.
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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
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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.
- Use this MCP plus 5,100+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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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.
Checking the Status of a Complex Automation Job
Today, checking job status means logging into the web portal, finding your specific automation setup among dozens of others, then clicking on the run history. You have to filter by date range and scroll through potentially hundreds of log entries just to confirm if the latest scrape was successful or if it failed because a dependency was missing.
With this MCP, you simply ask your agent to check the status for that job. It calls `list_runs` and gives you an immediate, clean summary in the chat window. You get confirmation on success rates and timing without ever touching the main console.
You Get Full Operational Context with Apify MCP
Before this, figuring out why a job failed meant checking multiple places: Did the schedule fire? Were the necessary webhooks active? Was the key-value store updated? It was clicking through four or five separate tabs just to piece together one failure point.
Now, your agent aggregates that data for you. You can list schedules, check tasks, and review runs—all in a single conversation thread. The system gives you the full operational picture, right when you need it.
What your AI can actually do with this
Okay, so if your team relies on automated scraping, managing that infrastructure is a pain. You're constantly checking logs and auditing results in a separate web console just to know if things are running right. This MCP changes that. It gives your AI agent direct access to the full scope of your automation ecosystem.
Instead of clicking through complex menus, you just ask it questions about your jobs and data. Your agent handles the rest: it can list every single setup you have, check how recently a job ran, or pull specific records from massive datasets instantly. Because Vinkius hosts this MCP, you connect once to your preferred AI client and get immediate operational control over all your cloud automation tools.
It means you spend less time checking dashboards and more time using the data.
019d8417-97d2-721c-87af-8219b00e28f0 Here's how it actually works
The bottom line is: you control complex automation workflows using plain conversation instead of technical consoles.
First, subscribe to this MCP on Vinkius and input your Apify API Key.
Next, talk to your AI client. You just need to tell it what you want to check—for example, 'Show me the status of the pricing crawler.'
The agent calls the necessary tools, retrieves the data (like listing runs or getting dataset items), and presents a clean summary back in the chat.
Who is this actually for?
This MCP is for Ops Engineers who hate logging into dashboards just to check a status. It’s for Data Scientists who need fast, auditable access to scraping results, and Growth Leads who need immediate visibility into scraped competitor data.
Uses the MCP to list actors or run lists to verify that new data pipelines are executing correctly before committing them.
Checks schedules and webhooks to confirm automated tasks will fire at the correct time, ensuring operational continuity.
Retrieves specific dataset items from successful scraping runs to quickly audit competitor pricing or market intelligence without downloading files.
What Changes When You Connect
Stop manual logging: Instead of navigating to a console, you can ask the agent to list all actors or audit tasks directly in your chat interface. It just works.
Deep visibility into data: You don't have to download massive files to check a few records. The agent uses get_dataset_items to pull specific pieces of scraped information instantly.
Verify job status quickly: Need to know if the scraping ran yesterday? Use list_runs and tell your agent which actor you need to check, getting immediate performance reports.
Understand dependencies: By listing webhooks or key-value stores, you can map out exactly how different parts of your automation workflow are connected without guessing.
Maintain control: You get a clear view of all operational components—schedules and tasks—using list_schedules and list_tasks, so nothing falls through the cracks.
See it in action
Debugging Data Flow
A data scientist needs to know why yesterday's competitor pricing dataset is incomplete. They ask their agent, which then uses list_runs to find the last successful execution and get_actor to check that specific actor’s details for potential errors.
Auditing Operational Health
An ops manager needs a quick report on all scheduled jobs. The agent uses list_schedules and then checks associated webhooks to confirm every automated process is set up with the proper alerts and triggers.
Quick Data Validation
A growth lead just ran a major scrape and needs to see if it captured any data on 'Product X'. They ask the agent, which uses list_datasets followed by get_dataset_items to confirm the presence and quality of the specific records.
Onboarding New Systems
A new team member needs to see what automation tools are available. They ask the agent to run list_actors, getting an immediate, comprehensive list of all running services without needing manual console access.
The honest tradeoffs
Checking status manually
Logging into the Apify dashboard, clicking on the actor, then navigating to the 'Runs' tab, filtering dates, and scrolling through logs just to find one success rate.
Instead, tell your agent to run list_runs for that specific actor. It gives you a summarized view of recent performance and success rates right in the chat.
Guessing data location
Assuming all scraped data is dumped into one place, forcing you to check every single dataset manually to find what you need.
Start by using list_datasets to see everything. Then ask the agent to use get_dataset_items on the correct ID and pull only the fields you care about.
Ignoring dependencies
Writing a new automation job without realizing it relies on an existing key-value store or webhook that hasn't been updated.
Always ask the agent to run list_key_value_stores and list_webhooks first. It maps out your current dependencies, so you build correctly.
When It Fits, When It Doesn't
Use this MCP if your job involves checking system state across multiple interconnected components (actors, schedules, datasets). You need to know if something happened and what the result was. If you only need basic information—say, 'What is my email address?' or 'List all files in Folder A'—then a general data retrieval tool will work just fine. But if you are dealing with complex automation cycles, where success depends on actors running, schedules triggering, and datasets accumulating, this MCP gives you the necessary operational depth. Don't use it if your goal is simply to create an actor; use the core Apify platform for that. Use this only for auditing and monitoring.
Questions you might have
How does `list_actors` help me? +
It provides an immediate list of every automation setup in your account. This lets you quickly see what services are running and which ones might be outdated or unused.
What is the difference between `list_runs` and `list_schedules`? +
list_runs shows actual historical job executions—what happened. list_schedules shows the rules for when jobs are set to run, so you know when they're supposed to fire.
Do I need to use `get_dataset_items` every time? +
No. You only use it when you need to audit or retrieve specific data points from a completed dataset, rather than just checking the count of records in that dataset.
`list_tasks` and `get_actor`—what's the relationship? +
You first use get_actor to get details on a whole setup. Then, you can use list_tasks to see the specific, granular steps that make up that overall automation process.
When I use `get_user_info`, what details can I retrieve about my account? +
It provides the core metadata for your authenticated Apify user. You get confirmation of the owner ID, API access limits, and overall billing status right away.
If a job fails, how do I troubleshoot it using `list_runs`? +
The run list shows not only success or failure, but also the specific error code and timestamp. You can check these details to narrow down whether the issue was an API problem or a script bug.
What kind of custom data can I manage with `list_key_value_stores`? +
This tool lets you list and audit your persistent, structured key-value data. You can track settings, configurations, or small amounts of application state that aren't part of the main datasets.
If I check my external integrations with `list_webhooks`, what should I look out for? +
You can see a list of all configured webhooks and their current endpoint status. This helps you monitor if your automated triggers are pointing to the correct, active URLs.
How do I find my Apify API Key? +
Log in to your Apify Console, and you will find your API Token under the Integrations tab. Copy and paste it below.
Can the agent check the results of a scrape? +
Yes. Use the get_dataset_items tool providing the Dataset ID. Your agent will retrieve the items from the cloud storage, allowing you to audit the output instantly.
Is it possible to list actor runs via the agent? +
Yes. The list_runs tool allows your agent to retrieve the history of executions for any specific actor, including durations and final statuses.
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