How do I create a Pingdom API Token?

In your Pingdom dashboard (or SolarWinds portal), go to **Settings** > **API Tokens**. Click **Add API Token**, give it a name, and ensure it has the necessary access levels.

Can I see response times for different global regions?

Yes! The `list_check_results` tool returns data from multiple probe locations, allowing you to see how your site performs from different parts of the world.

Does this support pausing multiple checks at once?

The tools handle checks individually, but you can ask the AI agent to 'Pause all checks related to our API' and it will process them sequentially using their IDs.

Pingdom MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Pingdom
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Pingdom through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Pingdom "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Pingdom?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Pingdom MCP Server

Connect your Pingdom account to any AI agent and take full control of your website monitoring and reliability workflows through natural conversation.

Pydantic AI validates every Pingdom tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Uptime Visibility — List all monitoring checks and retrieve real-time status (up, down, unconfirmed).
Performance Tracking — Fetch average response times and detailed outage history for any specific check.
Log Auditing — Retrieve raw check results to investigate specific errors or latency spikes.
Global Infrastructure Oversight — List all Pingdom probe locations to understand your monitoring coverage.
Alert Management — List notification contacts and pause or resume checks during maintenance windows.

The Pingdom MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Pingdom to Pydantic AI via MCP

Follow these steps to integrate the Pingdom MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Pingdom with type-safe schemas

Why Use Pydantic AI with the Pingdom MCP Server

Pydantic AI provides unique advantages when paired with Pingdom through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Pingdom integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Pingdom connection logic from agent behavior for testable, maintainable code

Pingdom + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Pingdom MCP Server delivers measurable value.

Type-safe data pipelines: query Pingdom with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Pingdom tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Pingdom and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Pingdom responses and write comprehensive agent tests

Pingdom MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Pingdom to Pydantic AI via MCP:

get_average_response_time

Get average response time for a check

get_check_details

Get details for a specific check

get_check_outages

List outages for a specific check

list_alert_contacts

List alert notification contacts

list_check_results

List individual check results/logs

list_maintenance_windows

List scheduled maintenance windows

list_pingdom_probes

List all Pingdom monitoring locations (probes)

list_uptime_checks

List all Pingdom uptime checks

pause_uptime_check

Pause a specific uptime check

resume_uptime_check

Resume a specific uptime check

Example Prompts for Pingdom in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Pingdom immediately.

"List all my current uptime checks and their status."

"What was the average response time for the 'Main Site' check (ID: 12345) today?"

"Pause the uptime check for ID 98765 for our scheduled maintenance."

Troubleshooting Pingdom MCP Server with Pydantic AI

Common issues when connecting Pingdom to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Pingdom + Pydantic AI FAQ

Common questions about integrating Pingdom MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Pingdom MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.