How to Use the WebScrapingAPI MCP in CrewAI

Q: Can I use WebScrapingAPI to gather product data within a CrewAI workflow?

Yes. You can have an 'Acquisition Agent' run scrapeecommerceproduct on different platforms and pass the structured JSON results to an 'Analysis Agent' for final reporting.

Q: Is WebScrapingAPI useful for general web research with CrewAI?

Definitely. Agents can use searchgoogleserp or customapiscrape to gather initial data points. The crew then coordinates the scraping, extraction, and synthesis of that information.

Coordinate specialized agents for autonomous research using CrewAI with WebScrapingAPI.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect WebScrapingAPI MCP to CrewAI

Create your Vinkius account to connect WebScrapingAPI to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup WebScrapingAPI with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Collaborative Market Research

Imagine Agent A running `search_google_serp` to find potential competitors' links. Agent B then uses those links and runs `scrape_and_auto_extract` to pull structured data like news headlines or product features. The agents share this extracted memory, building a complete dossier. This role-based specialization allows the crew to tackle multi-faceted research projects autonomously without needing human oversight at every step.

Comprehensive Product Comparison

Need to compare products across multiple channels? Assign Agent A to run `scrape_ecommerce_product` on Walmart, and assign Agent B to repeat the process for Amazon. The MCP Server ensures both tools pull structured JSON data (price, title) into a shared memory bank. The moderator agent can then analyze this combined dataset—a true autonomous operation.

Advanced Content Gathering

If your crew needs to gather deeply technical information, Agent C can use `scrape_js_rendered` on a complex documentation page. The MCP Server captures the full rendered state, and the results are passed to another agent for analysis. The system handles both static scrapes using `scrape_static_html` and deep JS rendering, making it powerful enough for any content source.

Setup guide

Set up WebScrapingAPI MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke WebScrapingAPI tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="WebScrapingAPI Analyst",
    goal="Access and analyze WebScrapingAPI data via MCP.",
    backstory="Expert analyst with direct WebScrapingAPI access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent WebScrapingAPI transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="WebScrapingAPI Analyst",
        goal="Access and analyze WebScrapingAPI data via MCP.",
        backstory="Expert analyst with direct WebScrapingAPI access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent WebScrapingAPI transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by WebScrapingAPI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect WebScrapingAPI now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about WebScrapingAPI MCP in CrewAI

The MCP Server exposes all 10 tools to the crew's shared memory. Agents can call specific functions—like `search_bing_serp` or `scrape_via_residential_proxy`—and pass the results directly to another agent for analysis.

Yes. You can have an 'Acquisition Agent' run `scrape_ecommerce_product` on different platforms and pass the structured JSON results to an 'Analysis Agent' for final reporting.

Definitely. Agents can use `search_google_serp` or `custom_api_scrape` to gather initial data points. The crew then coordinates the scraping, extraction, and synthesis of that information.

The moderator agent can be programmed to watch for tool failures. Upon failure, it can trigger a fallback strategy, like switching the target source from Google to Yandex via `search_yandex_serp`.

It handles public web content across multiple domains. Data types include structured search results (SERP), raw HTML, e-commerce product metadata, and extracted text from general scraped pages.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript