# How to integrate Pingdom MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Pingdom MCP with OpenAI Agents SDK",
  "toolkit": "Pingdom",
  "toolkit_slug": "pingdom",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/pingdom/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/pingdom/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:22:02.222Z"
}
```

## Introduction

This guide walks you through connecting Pingdom to the OpenAI Agents SDK 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 OpenAI Agents SDK 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 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:
- Get and set up your OpenAI and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for Pingdom
- Configure an AI agent that can use Pingdom as a tool
- Run a live chat session where you can ask the agent to perform Pingdom operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

## 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:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Pingdom project
- Some knowledge of Python or Typescript

### 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).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Pingdom.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only pingdom.
- The router checks the user's Pingdom connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Pingdom.
- This approach keeps things lightweight and lets the agent request Pingdom tools only when needed during the conversation.
```python
# Create a Pingdom Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["pingdom"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Pingdom
const session = await composio.create(userId as string, {
  toolkits: ['pingdom'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Pingdom. "
        "Help users perform Pingdom operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Pingdom. Help users perform Pingdom operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

  if (!text) {
    rl.prompt();
    return;
  }

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["pingdom"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Pingdom. "
        "Help users perform Pingdom operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['pingdom'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Pingdom. Help users perform Pingdom operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

    if (!text) {
      rl.prompt();
      return;
    }

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

main().catch((err) => {
  console.error('Fatal error:', err);
  process.exit(1);
});
```

## Conclusion

This was a starter code for integrating Pingdom MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Pingdom.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Pingdom MCP Agent with another framework

- [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)

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- [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.
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- [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.
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- [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 OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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.

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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)