Can I trigger an API check manually through the agent?

Yes! Use the `trigger_check_run` tool with the unique check ID. The agent will instruct Checkly to run the test immediately from its global network.

How do I see the performance metrics for my homepage check?

Use the `get_check_performance_metrics` tool with the check ID. Your agent will fetch detailed analytics including success rates and response time percentiles.

Where do I find my Checkly API Key and Account ID?

Log in to your Checkly account and navigate to **User Settings -> API Keys** for the key. The **Account ID** can be found in the **Account Settings** section.

Checkly MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Checkly
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Checkly 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 Checkly "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Checkly MCP Server

Connect your Checkly account to any AI agent and take full control of your application monitoring and synthetic testing through natural conversation. Streamline how you ensure the uptime and performance of your APIs and web apps.

Pydantic AI validates every Checkly tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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

Check Oversight — List and retrieve details for all API and Browser monitors natively
Live Execution — Manually trigger check runs to verify system health on-demand flawlessly
Performance Intelligence — Access detailed performance metrics and response times for any monitor securely
Alert Management — List and audit all configured alert channels (Slack, Email, PagerDuty) flawlessly
Reliability Tracking — Monitor heartbeat and cron jobs to ensure your background tasks are running flawlessly
System Metadata — Retrieve core account information and organizational structures directly within your workspace

The Checkly MCP Server exposes 8 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 Checkly to Pydantic AI via MCP

Follow these steps to integrate the Checkly 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 8 tools from Checkly with type-safe schemas

Why Use Pydantic AI with the Checkly MCP Server

Pydantic AI provides unique advantages when paired with Checkly 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 Checkly integration code

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

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

Checkly + Pydantic AI Use Cases

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

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

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

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

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

Checkly MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Checkly to Pydantic AI via MCP:

get_check_details

Get detailed information for a specific check

get_check_performance_metrics

Retrieve performance metrics for a specific check

get_checkly_account_info

Retrieve core account and organization metadata

list_check_groups

List groups of checks

list_checkly_alert_channels

List all configured alert channels (Slack, Email, PagerDuty, etc)

list_checkly_checks

List all API and Browser checks

list_checkly_heartbeats

List all heartbeat (cron) monitors

trigger_check_run

Manually trigger a check to run immediately

Example Prompts for Checkly in Pydantic AI

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

"List all my monitors in Checkly and their last status."

"Show me the response time graph for the 'Checkout Flow' check."

"Check the status of my heartbeat monitors."

Troubleshooting Checkly MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Checkly + Pydantic AI FAQ

Common questions about integrating Checkly 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 Checkly MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.