Can my AI start a data extraction and let me know when it finishes?

Yes! You can ask your agent to `run_robot` on a certain URL. The agent receives the newly created task ID and can immediately invoke `get_task` sequentially to poll for completion. Once successful, it calls `get_task_data` to dump the JSON right into your conversation without breaking workflow.

How do bulk operations work natively through the agent?

When you have a trained robot, you can feed the `run_bulk_task` command an array of different URLs. Browse AI's backend splits them virtually into independent task instances. Your agent tracks the bulk run via `get_bulk_task` and eventually pulls the mega JSON merge using `download_bulk_data`.

Is there a risk of my agent suddenly spending all my API credits?

You have complete visibility—the agent can query `list_credits` at any moment to inform you precisely of your available balance and monthly limit. Also, since tools execute your natural directives, your AI agent only triggers extractions you specifically prompt for.

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

Name: Browse AI
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Browse AI through Vinkius, pass the Edge URL in the `mcps` parameter and every Browse AI 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

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Browse AI Specialist",
    goal="Help users interact with Browse AI effectively",
    backstory=(
        "You are an expert at leveraging Browse AI 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 Browse AI "
        "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 Browse AI MCP Server

Connect your Browse AI account to any AI agent and take full control of your no-code web scraping operations through natural conversation.

When paired with CrewAI, Browse AI becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Browse AI 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

Robot Discovery — List all your trained extraction and monitoring robots along with their configuration details
Execute Scrapes — Trigger specific robots to run tasks on target URLs without lifting a finger
Data Retrieval — Instantly download the final extracted JSON data from any successfully completed task
Bulk Operations — Initiate multi-URL concurrent extractions and download the unified bulk datasets
Monitor Sync — Check the status of your active web change monitors
Quota Management — Retrieve your current API credits usage and monthly plan limits

The Browse AI 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 Browse AI to CrewAI via MCP

Follow these steps to integrate the Browse AI 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 Browse AI

Why Use CrewAI with the Browse AI MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Browse AI 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

Browse AI + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Browse AI 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 Browse AI, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Browse AI 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 Browse AI against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Browse AI MCP Tools for CrewAI (10)

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

download_bulk_data

Returns a JSON array where each element contains the capturedData from one task. Download all extracted results from a completed Browse AI bulk run

get_bulk_task

Get bulk task execution status from Browse AI

get_robot

Get detailed configuration of a specific Browse AI robot

get_task

Check the status of a specific Browse AI extraction task

get_task_data

Only meaningful when the task status is "successful". Fields match the column names configured in the Browse AI robot builder hitting internal task references. Retrieve the final extracted JSON data from a successful Browse AI task

list_credits

Check Browse AI quota limits and credit usage

list_monitors

Monitors run on scheduled intervals to detect changes on target web pages and trigger notifications or data captures automatically via `/monitors`. List all active Browse AI web monitoring robots

list_robots

Each robot represents a no-code AI scraping workflow targeting a specific website or data pattern via `GET /robots`. List all Browse AI extraction and monitoring robots

run_bulk_task

Each set typically contains a different "originUrl". All extractions run concurrently on Browse AI infrastructure. Run a Browse AI robot in bulk mode across multiple URLs

run_robot

Pass a JSON string of input parameters (typically including "originUrl" for the target page and any variable fields the robot expects). Returns a taskId. Trigger a Browse AI robot to extract data from a target URL

Example Prompts for Browse AI in CrewAI

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

"List all my robots. Which ones are built for monitoring?"

"Run my HackerNews Scraper robot on the main page."

"Retrieve the JSON data for task t-78ab31."

Troubleshooting Browse AI MCP Server with CrewAI

Common issues when connecting Browse AI 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.

Browse AI + CrewAI FAQ

Common questions about integrating Browse AI 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.