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 CrewAI 8 tools — connect in under 2 minutes

Name: Checkly
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Checkly through Vinkius, pass the Edge URL in the `mcps` parameter and every Checkly tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Checkly Specialist",
    goal="Help users interact with Checkly effectively",
    backstory=(
        "You are an expert at leveraging Checkly tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Checkly "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Checkly becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Checkly tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Checkly MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 8 tools from Checkly

Why Use CrewAI with the Checkly MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Checkly through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Checkly + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Checkly for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Checkly, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Checkly tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Checkly against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Checkly MCP Tools for CrewAI (8)

These 8 tools become available when you connect Checkly to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Checkly + CrewAI FAQ

Common questions about integrating Checkly MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.