# How to integrate Pingdom MCP with CrewAI ```json { "title": "How to integrate Pingdom MCP with CrewAI", "toolkit": "Pingdom", "toolkit_slug": "pingdom", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/pingdom/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/pingdom/framework/crew-ai.md", "updated_at": "2026-05-12T10:22:02.222Z" } ``` ## 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 all uptime checks for your sites, show account credit and api usage left, fetch all alerting contacts with details 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 - [OpenAI Agents SDK](https://composio.dev/toolkits/pingdom/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/pingdom/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/pingdom/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/pingdom/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/pingdom/framework/codex) - [OpenClaw](https://composio.dev/toolkits/pingdom/framework/openclaw) - [Hermes](https://composio.dev/toolkits/pingdom/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/pingdom/framework/cli) - [Google ADK](https://composio.dev/toolkits/pingdom/framework/google-adk) - [LangChain](https://composio.dev/toolkits/pingdom/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/pingdom/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/pingdom/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/pingdom/framework/llama-index) ## 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. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PINGDOM_GET_ACTIONS_ALERTS` | Get Pingdom Alert Actions | Retrieves configured alert actions (notifications) from your Pingdom account. Alert actions define how and where notifications are sent when checks trigger alerts (e.g., email, SMS, webhooks, integrations like Slack/PagerDuty). Use this to list all actions or filter by specific checks, users, delivery channels, or time ranges. Supports pagination for large result sets. | | `PINGDOM_GET_CHECKS_LIST` | Get Checks List | Retrieves a list of all uptime/monitoring checks configured in Pingdom with optional filtering and pagination. Use this to: view all monitoring checks, filter by status/type/tags, search by name, or paginate through large check lists. Returns check details including ID, name, hostname, status, type, resolution, and optional tags. | | `PINGDOM_GET_CONTACT_DETAILS` | 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. Use this when you need complete information about a contact's notification configuration. | | `PINGDOM_GET_CONTACTS` | Get Contacts | Tool to retrieve all alerting contacts. Use when you need to list every contact along with their notification targets after establishing a Pingdom session. | | `PINGDOM_GET_CREDITS` | Get Credits | Retrieves comprehensive account information including check limits, SMS credits, and resource usage. Use this to monitor available checks (uptime and transaction), SMS credits, RUM sites, and alerting user capacity. Returns current usage counts and available slots for all resource types. | | `PINGDOM_GET_LIST_MAINTENANCE_OCCURRENCES` | List Maintenance Occurrences | Tool to list maintenance occurrences. Use when you need occurrences filtered by time range or a specific maintenance window ID. | | `PINGDOM_GET_MAINTENANCE_WINDOWS` | Get Maintenance Windows | Tool to retrieve a list of maintenance windows. Use when you need to list user's maintenance windows with optional pagination and time range filters. | | `PINGDOM_GET_PROBES` | Get Probes | Retrieves the complete list of Pingdom probe servers worldwide. This action returns all available probe servers that can be used for monitoring checks. Probes are distributed globally across regions (NA, EU, APAC, LATAM) and provide information about their location, IP addresses (IPv4 and IPv6), and availability status. Use this when you need to: - List all available monitoring locations - Select probes for creating uptime or transaction checks - Identify probe servers by region or country - Get IP addresses of probe servers for allowlisting | | `PINGDOM_GET_REFERENCE_DATA` | Get Reference Data | Retrieves Pingdom reference data including regions, timezones, datetime formats, number formats, and countries. This data is used for configuring Pingdom account settings, checks, and understanding available formatting options. Use this when you need to know valid timezone IDs, region configurations, or country codes for Pingdom operations. | | `PINGDOM_GET_SINGLE_CHECK` | Get Single Check | Perform a single on-demand Pingdom check against a target host. This executes an immediate test from a specified probe (or random probe if not specified) and returns the result. Use this when you need a quick connectivity or performance test of a website, server, or service. Example uses: "Test if google.com is reachable", "Check response time for example.com from a specific region", "Verify HTTP status of api.mysite.com". | | `PINGDOM_GET_TEAM_DETAILS` | Get Team Details | Tool to fetch detailed information for a specific alerting team. Use after listing teams to get full members and integrations details. | | `PINGDOM_GET_TEAMS` | Get Teams | Tool to retrieve all alerting teams and their members. Use after authenticating to Pingdom to manage team configurations. | | `PINGDOM_GET_TMS_TRANSACTION_CHECKS_LIST` | Get TMS Transaction Checks List | Retrieves a paginated list of all transaction (TMS) checks configured in Pingdom. Transaction checks (also called TMS checks) are synthetic monitoring tests that simulate user interactions with web applications by executing scripted sequences of actions. Use this action to: - Get an overview of all configured transaction checks - Retrieve check IDs, names, types, and current status - Paginate through large lists of transaction checks Returns an empty list if no transaction checks are configured. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Pingdom MCP server is an implementation of the Model Context Protocol that connects your AI agent to Pingdom. It provides structured and secure access so your agent can perform Pingdom operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 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 ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies **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 ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables 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 ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **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 ```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") ``` ### 5. Create a Composio Tool Router session for Pingdom **What's happening:** - You create a Pingdom only session through Composio - Composio returns an MCP HTTP URL that exposes Pingdom tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["pingdom"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **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. ```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, ) ``` ### 7. Create a CLI Chatloop and define the Crew **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. ```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") ``` ## Complete Code ```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 ## How to build Pingdom MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/pingdom/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/pingdom/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/pingdom/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/pingdom/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/pingdom/framework/codex) - [OpenClaw](https://composio.dev/toolkits/pingdom/framework/openclaw) - [Hermes](https://composio.dev/toolkits/pingdom/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/pingdom/framework/cli) - [Google ADK](https://composio.dev/toolkits/pingdom/framework/google-adk) - [LangChain](https://composio.dev/toolkits/pingdom/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/pingdom/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/pingdom/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/pingdom/framework/llama-index) ## Related Toolkits - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences. - [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses. - [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure. - [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure. - [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction. - [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting. - [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache. - [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence. - [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place. - [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines. - [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure. - [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code. - [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows. - [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights. - [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews. - [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease. - [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Pingdom MCP? 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. ### 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 Pingdom tools. ### Can I manage the permissions and scopes for Pingdom while using Tool Router? 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. ### 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 Pingdom data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)