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How to integrate Pingdom MCP with CrewAI

Connect CrewAI to Pingdom MCP. List all uptime checks for your sites, show account credit and api usage left, and more using natural language, with authentication handled for you.

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Pingdom is a web performance monitoring service for websites, servers, and applications. It helps you track uptime, performance, and get alerts when things go down.

13 Tools

Introduction

This guide walks you through connecting Pingdom to CrewAI using the Composio tool router. By the end, you'll have a working Pingdom agent that can list uptime and transaction checks, view maintenance windows, look up alert contacts and teams, and check your credits through natural language commands.

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

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

Also integrate Pingdom with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKGrok BotCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexPydantic AIAutoGen

TL;DR

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

The Pingdom MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Pingdom account. It provides structured and secure access to your monitoring data, so your agent can perform actions like retrieving uptime checks, managing alerts and contacts, viewing maintenance windows, and running immediate availability tests on your behalf.

  • Comprehensive uptime and check monitoring: Instantly fetch overviews of all your uptime checks, retrieve details for specific checks, and keep tabs on your website and server performance.
  • Alert action and contact management: Ask your agent to list all alerting actions, fetch contacts, or get detailed notification configurations for each contact in your Pingdom account.
  • Maintenance window tracking: Let your agent list and filter scheduled maintenance windows and occurrences, helping you plan downtime and track monitoring exceptions.
  • Immediate single-site checks: Perform real-time availability or performance tests on any host or URL directly from your agent, using specific probes and check types.
  • Reference data and credits insight: Retrieve essential reference lists (like time zones, probes, and contact types) and check your API credit and rate-limit status to stay informed and proactive.

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 Pingdom 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 Pingdom 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 Pingdom MCP URL
6

Create a Composio Tool Router session for Pingdom

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

url = session.mcp.url
What's happening:
  • You create a Pingdom only session through Composio
  • Composio returns an MCP HTTP URL that exposes Pingdom 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 Pingdom 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=["pingdom"],
)
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 Pingdom through Composio's Tool Router. The agent can perform Pingdom 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 Pingdom action and event your agent gets out of the box.

Get Pingdom Alert Actions

Requests generated alert actions for the Pingdom account, with check, user, channel and time parameters.

Get Checks List

Retrieves a list of all uptime/monitoring checks configured in Pingdom with optional filtering and pagination.

Get Contact Details

Retrieves comprehensive details of a specific Pingdom alerting contact by ID, including all configured notification methods (email, SMS), team memberships, contact type, and pause status.

Get Contacts

Retrieves alerting contacts and their notification targets from Pingdom.

Get Credits

Retrieves Pingdom account credits and resource usage, including check and SMS information.

List Maintenance Occurrences

Lists Pingdom maintenance occurrences, with optional time-range and maintenance window ID parameters.

Get Maintenance Windows

Lists a user's Pingdom maintenance windows, with optional pagination and time-range parameters.

Get Probes

Lists Pingdom probe servers used for uptime and transaction checks, with location, IP address and availability information.

Get Reference Data

Retrieves Pingdom reference data including regions, timezones, datetime formats, number formats, and countries.

Get Single Check

Perform a single on-demand Pingdom check against a target host.

Get Team Details

Tool to fetch detailed information for a specific alerting team.

Get Teams

Tool to retrieve all alerting teams and their members.

Get TMS Transaction Checks List

Lists Pingdom transaction (TMS) checks in a paginated overview, including their IDs, names, types and statuses.

FRAMEWORKS

How to build Pingdom MCP Agent with another framework

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Claude Agent SDK

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Grok Bot logo

Grok Bot

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OpenClaw logo

OpenClaw

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Hermes logo

Hermes

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Atomic Agent logo

Atomic Agent

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Google ADK logo

Google ADK

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LangChain logo

LangChain

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Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

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LlamaIndex logo

LlamaIndex

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Pydantic AI logo

Pydantic AI

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AutoGen logo

AutoGen

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FAQ

Frequently asked questions

With a standalone Pingdom MCP server, the agents and LLMs can only access a fixed set of Pingdom tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Pingdom 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 Pingdom tools.

Yes, absolutely. You can configure which Pingdom 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 Pingdom data and credentials are handled as safely as possible.

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