Checkly MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Checkly as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Checkly. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Checkly?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Checkly tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Checkly MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Checkly MCP Server
LlamaIndex provides unique advantages when paired with Checkly through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Checkly tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Checkly tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Checkly, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Checkly tools were called, what data was returned, and how it influenced the final answer
Checkly + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Checkly MCP Server delivers measurable value.
Hybrid search: combine Checkly real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Checkly to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Checkly for fresh data
Analytical workflows: chain Checkly queries with LlamaIndex's data connectors to build multi-source analytical reports
Checkly MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Checkly to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Checkly to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCheckly + LlamaIndex FAQ
Common questions about integrating Checkly MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Checkly with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Checkly to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.