How to Use the HyperDX (Open Source Observability) MCP in LlamaIndex

Q: How does LlamaIndex query logs from HyperDX (Open Source Observability)?

The MCP integration converts the output of listlogs into a format LlamaIndex can process. It builds a searchable index from the telemetry returned by the server.

Q: How do I prevent the agent from hallucinating alert rules?

Ground the agent in your current alert state by calling listalerts first. LlamaIndex uses this data to ensure any new rule creation is contextually valid.

Index your infrastructure telemetry into LlamaIndex for semantic search and RAG-based troubleshooting.

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Connect HyperDX (Open Source Observability) MCP to LlamaIndex

Create your Vinkius account to connect HyperDX (Open Source Observability) 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 HyperDX (Open Source Observability) with LlamaIndex

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Semantic log search with LlamaIndex

Feed your log history into a vector store to make your observability data queryable. Use `list_logs` to grab raw data points that LlamaIndex then indexes for semantic retrieval. This lets you ask questions about past incidents in plain English. Your agent finds relevant patterns across thousands of log lines that you would otherwise miss during a manual search.

Context-aware alert management

Store your alert definitions in a searchable index alongside your documentation. Use `list_alerts` to populate your knowledge base and keep your agent informed about current system constraints. This ensures your agent knows exactly what triggers an alert before it tries to tune thresholds. It prevents the agent from creating redundant rules that conflict with existing ones.

Unified dashboard indexing

Convert your dashboard layouts and metric configurations into queryable knowledge. Use `list_dashboards` and `get_dashboard` to ingest your current observability setup into your RAG pipeline. Your agent can now answer questions about your system architecture by referencing your actual dashboards. It grounds the agent's reasoning in your real-time infrastructure state.

Setup guide

Set up HyperDX (Open Source Observability) 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 HyperDX (Open Source Observability) 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 HyperDX (Open Source Observability) tools.",
)
response = await agent.run("List recent HyperDX (Open Source Observability) data")

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Common questions about HyperDX (Open Source Observability) MCP in LlamaIndex

The MCP integration converts the output of `list_logs` into a format LlamaIndex can process. It builds a searchable index from the telemetry returned by the server.

Absolutely. By pulling dashboard details through the MCP tools, LlamaIndex adds your infrastructure state to its knowledge base for more accurate answers.

The tool output is fetched live, then indexed into your vector store. This allows your agent to perform semantic search over historical logs and current configurations.

Ground the agent in your current alert state by calling `list_alerts` first. LlamaIndex uses this data to ensure any new rule creation is contextually valid.

The server establishes a direct, token-authenticated connection to your instance. Your logs are never stored by the MCP server itself, only passed to your index.

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