How to integrate Visualping MCP with Autogen

This guide walks you through connecting Visualping to AutoGen 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 AutoGen 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 AutoGen 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 AutoGen 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 AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Visualping
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Visualping tools
  • Run a live chat loop where you ask the agent to perform Visualping operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Visualping account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Visualping via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Visualping connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Visualping session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["visualping"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Visualping tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Visualping assistant agent with MCP tools
    agent = AssistantAgent(
        name="visualping_assistant",
        description="An AI assistant that helps with Visualping operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Visualping tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Visualping related question or task to the agent.\n")

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Visualping tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

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

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Visualping assistant agent with MCP tools
        agent = AssistantAgent(
            name="visualping_assistant",
            description="An AI assistant that helps with Visualping operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Visualping related question or task to the agent.\n")

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Visualping through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Visualping, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
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

CrewAI logo

CrewAI

Use Visualping MCP with CrewAI

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. Autogen 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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