How to integrate Builtwith MCP with CrewAI

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Introduction

This guide walks you through connecting Builtwith to CrewAI using the Composio tool router. By the end, you'll have a working Builtwith agent that can get all websites using shopify in 2024, show historical tech changes for example.com, find online stores selling nike shoes, list domains with similar tech to airbnb.com through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Builtwith account through Composio's Builtwith MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Builtwith connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Builtwith
  • Build a conversational loop where your agent can execute Builtwith operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

What is the Builtwith MCP server, and what's possible with it?

The Builtwith MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your BuiltWith account. It provides structured and secure access to web technology profiling and competitive intelligence data, so your agent can perform actions like analyzing website tech stacks, discovering competitor technologies, generating domain lists, and recommending similar sites on your behalf.

  • Comprehensive website technology profiling: Instantly analyze the current and historical tech stack of any website, including CMS, analytics, hosting, and more.
  • Bulk domain and technology list generation: Have your agent create and export lists of domains using specific technologies, or generate tailored files for large-scale research or outreach.
  • Competitor and product discovery: Identify financial, e-commerce, or product data for domains and uncover new market entrants or online retailers selling specific products.
  • Technology usage trends and datasets: Query historical datasets to track how specific technologies have been adopted across the internet over time, supporting market research and technology forecasting.
  • Intelligent site recommendations: Request suggestions for websites with similar technology profiles to a given domain, helping expand prospecting or research lists with relevant leads.

Supported Tools & Triggers

Tools
Create Domain List FileTool to create a txt or zip file from a list of domains.
Datasets LookupTool to access mass internet technology usage information from 2000 to today.
Domain API LookupTool to retrieve current and historical technology information of a website.
Financial API LookupTool to fetch financial data for a domain.
Free API LookupTool to access last updated dates and counts for technology groups and categories for websites.
Lists API Get List With MetaTool to retrieve a list of websites using a specified technology, including metadata.
Lists API Get ListTool to retrieve a list of websites using a specific technology.
MCP API LookupTool to query live web technologies for a root domain.
Product API LookupTool to find websites selling specific ecommerce products.
Recommendations API LookupTool to generate a list of websites with similar technology profiles.
Redirects API LookupTool to retrieve live and historical redirects for a website.
Social API LookupTool to retrieve domains associated with social media profile urls.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Builtwith connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Builtwith via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Builtwith MCP URL

Create a Composio Tool Router session for Builtwith

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["builtwith"],
)
url = session.mcp.url
What's happening:
  • You create a Builtwith only session through Composio
  • Composio returns an MCP HTTP URL that exposes Builtwith tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Builtwith Assistant",
    goal="Help users interact with Builtwith through natural language commands",
    backstory=(
        "You are an expert assistant with access to Builtwith tools. "
        "You can perform various Builtwith operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Builtwith MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Builtwith operations.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Builtwith related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_builtwith_agent.py

Complete Code

Here's the complete code to get you started with Builtwith and CrewAI:

python
# file: crewai_builtwith_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Builtwith session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["builtwith"],
    )
    url = session.mcp.url

    # Configure LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Builtwith assistant agent
    toolkit_agent = Agent(
        role="Builtwith Assistant",
        goal="Help users interact with Builtwith through natural language commands",
        backstory=(
            "You are an expert assistant with access to Builtwith tools. "
            "You can perform various Builtwith operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Builtwith operations.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Builtwith related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Builtwith through Composio's Tool Router. The agent can perform Builtwith operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

How to build Builtwith MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Builtwith MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Builtwith MCP with Claude Agent SDK

Google ADK

Google ADK

Use Builtwith MCP with Google ADK

LangChain

LangChain

Use Builtwith MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Builtwith MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Builtwith MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Builtwith MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Builtwith MCP?

With a standalone Builtwith MCP server, the agents and LLMs can only access a fixed set of Builtwith tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Builtwith and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Builtwith tools.

Can I manage the permissions and scopes for Builtwith while using Tool Router?

Yes, absolutely. You can configure which Builtwith scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Builtwith data and credentials are handled as safely as possible.

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