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.

Apify MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Apify
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Apify through Vinkius, pass the Edge URL in the `mcps` parameter and every Apify tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Apify Specialist",
    goal="Help users interact with Apify effectively",
    backstory=(
        "You are an expert at leveraging Apify tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Apify "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Apify MCP Server

Connect your Apify workspace to your AI agent and seamlessly direct full-stack web scraping and data extraction workflows through natural conversation.

When paired with CrewAI, Apify becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Apify tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Discover & Run Actors — Browse all scraper bots (Actors) available in your account. Fire them off asynchronously or synchronously for fast, targeted scraping
Extract Datasets — Pull robust structured data formats out of completed runs. Retrieve detailed JSON records directly into the agent's context window
Fetch Key-Value Stores — Programmatically read snapshots, cached HTML pages, or screenshots from the Apify Key-Value repositories mapped to a run
Job Control & Scalability — Stop hanging scraper jobs, queue new dynamic URLs mid-run, or inspect deep usage analytics, compute units, and webhooks limits

The Apify MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Apify to CrewAI via MCP

Follow these steps to integrate the Apify MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Apify

Why Use CrewAI with the Apify MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Apify through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Apify + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Apify MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Apify for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Apify, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Apify tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Apify against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Apify MCP Tools for CrewAI (10)

These 10 tools become available when you connect Apify to CrewAI via MCP:

abort_run

Any data already scraped and pushed to the dataset is preserved. The run status changes to ABORTED. Use this to stop runaway scrapes or when sufficient data has been collected. Graceful shutdown depends on the actor implementation. Abort an active Apify actor run

get_account_limits

Essential for monitoring consumption and avoiding overage charges. Check Apify account subscription limits and compute unit usage

get_dataset_items

The datasetId is found in the run object (defaultDatasetId). Supports pagination via limit (max items per page) and offset (starting position). Returns an array of JSON objects containing the scraped data fields. Use limit=1000 for bulk downloads. Export structured JSON data from an Apify dataset

get_key_value_store

Key-value stores hold arbitrary data like screenshots (OUTPUT), configuration files, or intermediate results. The storeId comes from the run object (defaultKeyValueStoreId). Common keys include "OUTPUT", "INPUT", and "SCREENSHOT". Retrieve an item from an Apify actor key-value store

get_run

Poll this endpoint to track long-running scrapes. Check the status and metadata of a specific Apify actor run

list_actors

Includes owned actors and those from the Apify Store that have been saved. Each entry contains the actorId, name, description, and default run configuration. Use the actorId to trigger runs. List all accessible actors in the Apify account

list_webhooks

RUN.SUCCEEDED, ACTOR.RUN.FAILED), target URLs, and associated actor IDs. Webhooks enable event-driven architectures by notifying external systems when actor runs complete or fail. List all configured webhooks in the Apify account

push_to_queue

Pass the queueId (from the run object) and a JSON string array of request objects, e.g., [{"url":"https://...","uniqueKey":"..."}]. This enables dynamic crawling where new pages are discovered and added during execution. Dynamically push new URLs to an active Apify request queue

run_actor

Pass the actorId (e.g., "apify/web-scraper" or a custom ID) and a JSON string with the input configuration (start URLs, proxy settings, max pages, etc.). Returns immediately with a runId. Use ap.get_run to poll for completion and ap.get_dataset_items to retrieve extracted data. Start an Apify actor asynchronously with custom JSON input

run_actor_sync

run_actor but waits for the actor to finish before returning. The response includes the full run object with defaultDatasetId for immediate data retrieval. Best for short-lived actors (under 5 minutes). For long-running scrapes, use the async ap.run_actor instead. Run an Apify actor and block until completion (synchronous)

Example Prompts for Apify in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Apify immediately.

"List all the Apify actors available on my account."

"Verify the status of run 'qKpwH9LgC3r0Xm' and show me its final dataset if finished."

"How are our compute usage limits tracking this current month on Apify?"

Troubleshooting Apify MCP Server with CrewAI

Common issues when connecting Apify to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Apify + CrewAI FAQ

Common questions about integrating Apify MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.