# How to integrate Pingdom MCP with Vercel AI SDK v6

```json
{
  "title": "How to integrate Pingdom MCP with Vercel AI SDK v6",
  "toolkit": "Pingdom",
  "toolkit_slug": "pingdom",
  "framework": "Vercel AI SDK",
  "framework_slug": "ai-sdk",
  "url": "https://composio.dev/toolkits/pingdom/framework/ai-sdk",
  "markdown_url": "https://composio.dev/toolkits/pingdom/framework/ai-sdk.md",
  "updated_at": "2026-05-12T10:22:02.222Z"
}
```

## Introduction

This guide walks you through connecting Pingdom to Vercel AI SDK v6 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 Vercel AI 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

- [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)
- [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 and configure a Vercel AI SDK agent with Pingdom integration
- Using Composio's Tool Router to dynamically load and access Pingdom tools
- Creating an MCP client connection using HTTP transport
- Building an interactive CLI chat interface with conversation history management
- Handling tool calls and results within the Vercel AI SDK framework

## What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.
Key features include:
- streamText: Core function for streaming responses with real-time tool support
- MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
- Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
- OpenAI Provider: Native integration with OpenAI models

## 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 you begin, make sure you have:
- Node.js and npm installed
- A Composio account with API key
- An OpenAI API key

### 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 required dependencies

First, install the necessary packages for your project.
What you're installing:
- @ai-sdk/openai: Vercel AI SDK's OpenAI provider
- @ai-sdk/mcp: MCP client for Vercel AI SDK
- @composio/core: Composio SDK for tool integration
- ai: Core Vercel AI SDK
- dotenv: Environment variable management
```bash
npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's needed:
- OPENAI_API_KEY: Your OpenAI API key for GPT model access
- COMPOSIO_API_KEY: Your Composio API key for tool access
- COMPOSIO_USER_ID: A unique identifier for the user session
```bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
```

### 4. Import required modules and validate environment

What's happening:
- We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
- The dotenv/config import automatically loads environment variables
- The MCP client import enables connection to Composio's tool server
```typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});
```

### 5. Create Tool Router session and initialize MCP client

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 mcp object contains the URL and authentication headers needed to connect to the MCP server
- This session provides access to all Pingdom-related tools through the MCP protocol
```typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["pingdom"],
  });

  const mcpUrl = session.mcp.url;
```

### 6. Connect to MCP server and retrieve tools

What's happening:
- We're creating an MCP client that connects to our Composio Tool Router session via HTTP
- The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
- The type: "http" is important - Composio requires HTTP transport
- tools() retrieves all available Pingdom tools that the agent can use
```typescript
const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();
```

### 7. Initialize conversation and CLI interface

What's happening:
- We initialize an empty messages array to maintain conversation history
- A readline interface is created to accept user input from the command line
- Instructions are displayed to guide the user on how to interact with the agent
```typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to pingdom, like summarize my last 5 emails, send an email, etc... :)))\n",
);

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();
```

### 8. Handle user input and stream responses with real-time tool feedback

What's happening:
- We use streamText instead of generateText to stream responses in real-time
- toolChoice: "auto" allows the model to decide when to use Pingdom tools
- stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
- onStepFinish callback displays which tools are being used in real-time
- We iterate through the text stream to create a typewriter effect as the agent responds
- The complete response is added to conversation history to maintain context
- Errors are caught and displayed with helpful retry suggestions
```typescript
rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

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

  messages.push({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

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

## Complete Code

```typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["pingdom"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to pingdom, like summarize my last 5 emails, send an email, etc... :)))\n",
  );

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
      console.log("\nGoodbye!");
      rl.close();
      process.exit(0);
    }

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

    messages.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

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

## Conclusion

You've successfully built a Pingdom agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.
Key features of this implementation:
- Real-time streaming responses for a better user experience with typewriter effect
- Live tool execution feedback showing which tools are being used as the agent works
- Dynamic tool loading through Composio's Tool Router with secure authentication
- Multi-step tool execution with configurable step limits (up to 10 steps)
- Comprehensive error handling for robust agent execution
- Conversation history maintenance for context-aware responses
You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## 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)
- [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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- [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.
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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 Vercel AI SDK v6?

Yes, you can. Vercel AI SDK v6 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) · [Composio docs](https://docs.composio.dev/llms.txt)
