Can I scrape a URL directly without creating a scraper first?

Yes! Use the `direct_scrape` tool and provide the URL. It will use Browserhub's real browsers to render the page and extract data based on your parameters instantly.

How do I monitor the progress of a scraping job?

Simply ask the agent to `get_scraping_job` and provide the Job ID. It will return the current status (e.g., pending, running, finished) and the result data once ready.

Can I specify a proxy location for my scraping request?

Yes. When using `direct_scrape`, you can provide a `proxy_country` code. Use the `list_proxy_locations` tool to see the full list of supported countries in your account.

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

Name: Browserhub
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Browserhub through Vinkius, pass the Edge URL in the `mcps` parameter and every Browserhub 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="Browserhub Specialist",
    goal="Help users interact with Browserhub effectively",
    backstory=(
        "You are an expert at leveraging Browserhub 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 Browserhub "
        "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)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Browserhub MCP Server

Connect your Browserhub.io account to any AI agent and orchestrate your web scraping, data extraction, and proxy management workflows through natural conversation.

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

Scraper Oversight — List all your configured scrapers and blueprints, and retrieve detailed metadata for each.
Job Execution — Trigger scraping jobs with dynamic URL overrides and monitor their progress in real-time.
Direct Scraping — Perform one-off URL extractions using real browsers without pre-defined scrapers.
Data Retrieval — Retrieve structured data captured by your jobs directly into your workspace.
Infrastructure Management — List available proxy locations and check your account credit balance.
Task History — List and inspect all your previous scraping jobs.

The Browserhub 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 Browserhub to CrewAI via MCP

Follow these steps to integrate the Browserhub 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 Browserhub

Why Use CrewAI with the Browserhub MCP Server

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

Browserhub + CrewAI Use Cases

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

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

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

Browserhub MCP Tools for CrewAI (10)

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

direct_scrape

Perform a one-off URL scrape without a pre-defined scraper

get_account_balance

Check account credit balance

get_blueprint

Get details of a specific blueprint

get_scraper

Get details of a specific scraper

get_scraping_job

Get status and results of a scraping job

list_blueprints

List all scraper blueprints

list_proxy_locations

List all available proxy locations

list_scrapers

List all configured scrapers

list_scraping_jobs

List all scraping jobs

run_scraper

Start a scraping job using a specific scraper

Example Prompts for Browserhub in CrewAI

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

"List all my configured scrapers in Browserhub."

"Scrape the URL https://example.com using the 'E-commerce' scraper."

"Check my Browserhub account credit balance."

Troubleshooting Browserhub MCP Server with CrewAI

Common issues when connecting Browserhub 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.

Browserhub + CrewAI FAQ

Common questions about integrating Browserhub 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.