How to Use the Hexometer MCP in LlamaIndex

Q: Can LlamaIndex query historical Hexometer alerts?

Absolutely. You fetch alerts via listrecentalerts and index them. Your agent then answers questions about what went wrong and when.

Index your live site performance data into LlamaIndex. Create a searchable knowledge base for your infrastructure health.

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Connect Hexometer MCP to LlamaIndex

Create your Vinkius account to connect Hexometer 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 Hexometer with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Semantic Search over Site Metrics

Query your site's history using natural language. LlamaIndex takes output from `get_uptime_stats` and indexes it so you can ask questions about past performance trends. Stop digging through logs. Your agent retrieves data from the index to provide context-aware answers about your current uptime and site status.

Unified Performance Knowledge

Combine `check_speed_desktop` results with your existing documentation. The agent builds a vector representation of your site's speed benchmarks. This creates a queryable performance history. You search for specific speed regressions by asking the agent to compare recent tool outputs stored in your index.

Automated Security and Audit Indexing

Store results from `get_security_status` and `run_seo_audit` directly into your knowledge base. Your agent uses this to answer questions about security compliance. When a site fails a check, the finding is indexed immediately. You query the knowledge base to see if a security warning is a recurring issue or a new event.

Setup guide

Set up Hexometer 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 Hexometer 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 Hexometer tools.",
)
response = await agent.run("List recent Hexometer data")

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Common questions about Hexometer MCP in LlamaIndex

Use the MCP tool spec to pull data from the server. Once the agent retrieves the results, you pass them to your ingestion pipeline to create a searchable index.

Absolutely. You fetch alerts via `list_recent_alerts` and index them. Your agent then answers questions about what went wrong and when.

Yes. By providing real-time site health data, your agent provides grounded answers instead of guessing based on static documents.

It is. You convert the tools to an async list and pass them to the agent constructor for seamless tool execution.

The server transmits only the requested diagnostic data. All data is encrypted in transit and resides within your local index once retrieved.

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