How to integrate Visualping MCP with CrewAI

This guide walks you through connecting Visualping to CrewAI using the Composio tool router. By the end, you'll have a working Visualping agent that can create page monitors, check visual changes, update your monitors, and delete monitors through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Visualping account through Composio's Visualping MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Visualping is a web page monitoring service that tracks visual, text, and content changes. Use it to catch important website updates early and get alerts when changes match your rules.

9 Tools

Introduction

This guide walks you through connecting Visualping to CrewAI using the Composio tool router. By the end, you'll have a working Visualping agent that can create page monitors, check visual changes, update your monitors, and delete monitors through natural language commands.

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

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

Also integrate Visualping with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndex

TL;DR

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

The Visualping MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Visualping account. It provides structured and secure access to your website monitors, workspaces, check history, and detected changes, so your agent can create monitors, review changes, update monitoring settings, estimate check costs, and launch checks on your behalf.

  • Website monitor creation: Have your agent create one or several website monitors with shared detection settings in your chosen workspace.
  • Monitor and change review: Instruct your agent to list and filter monitors, then inspect their configuration, check history, and detected changes.
  • Monitoring configuration updates: Let the agent change monitored URLs, schedules, detection rules, active state, or notification settings as your needs evolve.
  • On-demand website checks: Direct your agent to estimate the credits needed for checks, then launch immediate checks for selected active monitors.
  • Account and monitor management: Have your agent review account details, workspaces, plan limits, balances, and monitor counts, or permanently delete monitors you no longer need.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Visualping connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

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.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Visualping via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

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
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
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 Visualping MCP URL
6

Create a Composio Tool Router session for Visualping

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["visualping"])

url = session.mcp.url
What's happening:
  • You create a Visualping only session through Composio
  • Composio returns an MCP HTTP URL that exposes Visualping tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[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:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["visualping"],
)
url = session.mcp.url

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

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

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

Conclusion

You now have a CrewAI agent connected to Visualping through Composio's Tool Router. The agent can perform Visualping 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
TOOLS

Supported Tools

Every Visualping action and event your agent gets out of the box.

Bulk Create Monitors

Create two or more active monitors with shared detection settings.

Create Monitor

Create a monitor in a workspace.

Delete Monitor

Permanently delete one monitor from its Visualping workspace.

Describe Account and Workspaces

Return the connected Visualping account, organization, workspaces, plan limits, balances, and monitor counts.

Get Monitor

Get a monitor's configuration, check history, and detected changes in one response.

Launch Monitor Checks

Immediately launch checks for one or more active monitors, consuming or reserving monitoring credits.

List Monitors

List and filter monitors visible to the connected workspace, returning one page and a continuation cursor.

Preflight Monitor Checks

Estimate the credits required to check one or more active monitors without launching a check or changing their schedules.

Update Monitor

Partially update a monitor's core URL, schedule, detection, state, or notification settings.

FRAMEWORKS

How to build Visualping MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Visualping MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Visualping MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Visualping MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Visualping MCP with Claude Cowork

Codex logo

Codex

Use Visualping MCP with Codex

OpenClaw logo

OpenClaw

Use Visualping MCP with OpenClaw

Hermes logo

Hermes

Use Visualping MCP with Hermes

CLI logo

CLI

Use Visualping MCP with CLI

Google ADK logo

Google ADK

Use Visualping MCP with Google ADK

LangChain logo

LangChain

Use Visualping MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Visualping MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Visualping MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Visualping MCP with LlamaIndex

FAQ

Frequently asked questions

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

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 Visualping tools.

Yes, absolutely. You can configure which Visualping 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.

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 Visualping data and credentials are handled as safely as possible.

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