How to Use the Checkly MCP in CrewAI

Q: How do I restrict which Checkly tools a CrewAI agent can use?

Use the MCPServerHTTP class from crewai.mcp and apply a toolfilter. This lets you block write operations like triggercheckrun while keeping read-only metrics open.

Q: What is the easiest way to connect CrewAI to my Checkly account?

Pass your Vinkius endpoint URL directly into the mcps list when initializing your agent. This automatically discovers and registers all active monitoring tools.

Deploy specialized CrewAI agent teams to monitor, test, and triage Checkly issues autonomously.

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Connect Checkly MCP to CrewAI

Create your Vinkius account to connect Checkly to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Checkly with CrewAI

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Multi-agent collaboration for Checkly alerts

`list_checkly_alert_channels` identifies where your team receives system alerts, allowing a CrewAI dispatcher agent to coordinate responses. At the same time, a separate analyzer agent uses `list_checkly_checks` to locate the exact endpoints that triggered the alarm. This setup relies on CrewAI's shared memory. The analyzer agent passes the broken check IDs to the dispatcher agent, which then runs diagnostic tools sequentially without human intervention.

Autonomous performance debugging with this MCP Server

`get_check_performance_metrics` pulls raw latency and response times so your specialized QA agent can analyze trends. If it spots a spike, the agent calls `get_check_details` to check if recent config changes caused the slowdown. You configure this by adding the Vinkius URL directly to the `mcps` array in your agent definition. This gives your crew instant access to monitoring statistics without writing custom API wrappers.

Cron and heartbeat failure triage

`list_checkly_heartbeats` tracks your background cron jobs to verify they check in on time. When a heartbeat goes silent, a moderator agent uses `trigger_check_run` to force an immediate test and confirm the outage. To limit what your agents can do, use the `MCPServerHTTP` class from `crewai.mcp` with a `tool_filter`. This restricts your autonomous crew to read-only tools or specific test suites.

Setup guide

Set up Checkly MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Checkly tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Checkly Analyst",
    goal="Access and analyze Checkly data via MCP.",
    backstory="Expert analyst with direct Checkly access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Checkly transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Checkly Analyst",
        goal="Access and analyze Checkly data via MCP.",
        backstory="Expert analyst with direct Checkly access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Checkly transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Checkly. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Checkly MCP in CrewAI

Use the `MCPServerHTTP` class from `crewai.mcp` and apply a `tool_filter`. This lets you block write operations like `trigger_check_run` while keeping read-only metrics open.

Yes, CrewAI's shared memory allows agents to pass tool results. One agent can fetch raw data using `get_check_performance_metrics` and share it with a moderator agent for analysis.

Pass your Vinkius endpoint URL directly into the `mcps` list when initializing your agent. This automatically discovers and registers all active monitoring tools.

It supports stdio, SSE, and Streamable HTTP. You can run your crew locally in your terminal or host it as a background service.

No logs are persisted. Vinkius runs the MCP server in a zero-trust, ephemeral sandbox, ensuring your performance metrics and account details are processed strictly in-memory.

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