# How to integrate Pingdom MCP with Pydantic AI ```json { "title": "How to integrate Pingdom MCP with Pydantic AI", "toolkit": "Pingdom", "toolkit_slug": "pingdom", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/pingdom/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/pingdom/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:22:02.222Z" } ``` ## Introduction This guide walks you through connecting Pingdom to Pydantic AI 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 Pydantic AI 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) - [CrewAI](https://composio.dev/toolkits/pingdom/framework/crew-ai) ## 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 Pingdom - 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 Pingdom 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 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 with an active API key - Basic familiarity with Python and async programming ### 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 Install the required libraries. What's happening: - composio connects your agent to external SaaS tools like Pingdom - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables 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 ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Pingdom - MCPServerStreamableHTTP connects to the Pingdom MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```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() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Pingdom 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 ```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 Pingdom composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["pingdom"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Pingdom endpoint - The agent uses GPT-5 to interpret user commands and perform Pingdom operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent pingdom_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[pingdom_mcp], instructions=( "You are a Pingdom assistant. Use Pingdom tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Pingdom API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```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 Pingdom.\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() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```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 Pingdom composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["pingdom"], ) 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 pingdom_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[pingdom_mcp], instructions=( "You are a Pingdom assistant. Use Pingdom 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 Pingdom.\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 Pingdom through Composio's Tool Router. With this setup, your agent can perform real Pingdom 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 + Pingdom for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## 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) - [CrewAI](https://composio.dev/toolkits/pingdom/framework/crew-ai) ## 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 Pydantic AI? 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 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)