How to Use the Checkly MCP in LlamaIndex

Q: How do I integrate Checkly with LlamaIndex?

Run pip install llama-index-tools-mcp first. Set up a BasicMCPClient pointing to the Vinkius URL, then wrap it in an McpToolSpec. Pass the async tool list to your FunctionAgent.

Q: Can LlamaIndex query Checkly performance data?

Your agent indexes data from getcheckperformancemetrics. You can then ask natural language questions about API latency or regional degradation. The answers come directly from your live metrics.

Q: Does Checkly support the LlamaIndex includeresources flag?

Yes. Setting includeresources=True allows the client to read raw monitor configurations. This gives your RAG setup deep context into how your tests are structured.

Q: Can I group my metrics by service?

The listcheckgroups tool maps your individual monitors to their parent services. This helps the agent understand the relationship between different API endpoints.

Index your Checkly performance metrics and alert configurations into searchable LlamaIndex knowledge bases.

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Connect Checkly MCP to LlamaIndex

Create your Vinkius account to connect Checkly to LlamaIndex 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 LlamaIndex

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Index Checkly Status with LlamaIndex

The `list_checkly_checks` tool feeds your active API and browser monitors directly into your vector store. Your LlamaIndex application reads the current state of your infrastructure and embeds it alongside your runbooks. When someone queries the agent about a broken login flow, it knows exactly which checks are currently failing. Getting granular details requires one extra step. The agent uses `get_check_details` to pull the exact assertion failures or script errors for a specific monitor. It adds that context to the index, giving your team a queryable history of what broke and why.

Build RAG Apps on Performance Metrics

Querying response times relies on the `get_check_performance_metrics` tool. Your agent pulls latency data across different regions and indexes the results. You can ask your application how the checkout API performed during yesterday's traffic spike, and it answers using hard data instead of guessing. Grouping these metrics makes the data manageable. By calling `list_check_groups`, the agent maps out which services belong together. It understands that a slowdown in the payment group correlates with specific database queries, creating a grounded foundation for your RAG pipeline.

Search Your Alerting Setup via MCP Server

The `list_checkly_alert_channels` tool extracts your current Slack, Email, and PagerDuty routing rules. Your LlamaIndex agent indexes who gets pinged for what. You can ask the agent why the backend team missed a critical failure, and it will read the actual routing configuration to find the missing hook. Tracking background jobs works the same way. The `list_checkly_heartbeats` tool pulls the status of your cron monitors. Your knowledge base now includes real-time awareness of every scheduled task that failed to report in.

Setup guide

Set up Checkly MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Checkly MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Checkly tools.",
)
response = await agent.run("List recent Checkly data")

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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 LlamaIndex

Run `pip install llama-index-tools-mcp` first. Set up a `BasicMCPClient` pointing to the Vinkius URL, then wrap it in an `McpToolSpec`. Pass the async tool list to your `FunctionAgent`.

Your agent indexes data from `get_check_performance_metrics`. You can then ask natural language questions about API latency or regional degradation. The answers come directly from your live metrics.

Yes. Setting `include_resources=True` allows the client to read raw monitor configurations. This gives your RAG setup deep context into how your tests are structured.

The `list_check_groups` tool maps your individual monitors to their parent services. This helps the agent understand the relationship between different API endpoints.

Vinkius executes tool calls inside a zero-trust environment. When your indexer fetches `get_check_details`, the request passes through a stateless proxy. Your internal test scripts and assertion logic never rest on shared disks.

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