How to integrate Visualping MCP with Pydantic AI

This guide walks you through connecting Visualping to Pydantic AI 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 Pydantic AI 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 Pydantic AI 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 Pydantic AI 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:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Visualping
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Visualping workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Visualping
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
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

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Visualping
  • MCPServerStreamableHTTP connects to the Visualping MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Visualping
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["visualping"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Visualping tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
visualping_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[visualping_mcp],
    instructions=(
        "You are a Visualping assistant. Use Visualping tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Visualping endpoint
  • The agent uses GPT-5 to interpret user commands and perform Visualping operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Visualping.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Visualping API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Visualping
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["visualping"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    visualping_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[visualping_mcp],
        instructions=(
            "You are a Visualping assistant. Use Visualping tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Visualping.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Visualping through Composio's Tool Router. With this setup, your agent can perform real Visualping actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Visualping for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
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. Pydantic AI 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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