How to Use the Azure Log Analytics Workspace MCP in LlamaIndex

Q: How do I load Azure Log Analytics Workspace data into LlamaIndex?

Install llama-index-tools-mcp and configure a BasicMCPClient. Convert the server connection into a tool list using totoollistasync() and pass it to your FunctionAgent.

Q: What happens if the KQL query returns too much data?

You should instruct your agent to use the limit operator in its KQL strings. Bounding the results prevents token overflow during the embedding phase.

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Connect Azure Log Analytics Workspace MCP to LlamaIndex

Create your Vinkius account to connect Azure Log Analytics Workspace 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 Azure Log Analytics Workspace with LlamaIndex

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LlamaIndex Telemetry Indexing

Pulling historical telemetry with the `query_logs` tool lets you immediately index those events into your vector store. Stop guessing what happened during the last outage. Your RAG applications now have access to actual system behavior instead of just runbooks. The agent filters the data using standard KQL operations before ingestion. This means you only store the critical error traces or performance bottlenecks, keeping your LlamaIndex embeddings focused and cheap.

Grounded Diagnostic Answers

When answering questions about a CPU spike, your function agent uses the `query_logs` tool to grab live metrics for comparison. It searches the index first to find similar past incidents, reads the resolution, and then runs a live query. The context is always relevant. You get answers backed by hard data. The underlying MCP Server handles the Azure authentication. Your function agent just requests the logs and synthesizes the response.

Secure Table Scoping

Massive, accidental data pulls are impossible because the `query_logs` tool restricts access to a single authorized table. Your LlamaIndex tool spec only accepts the Kusto logic, like `| summarize count() by bin(TimeGenerated, 5m)`. The scope is tightly controlled. The engine prepends the table name automatically. You drop `McpToolSpec` into your code, and the agent cannot bypass this boundary, no matter what prompt the user provides.

Setup guide

Set up Azure Log Analytics Workspace 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 Azure Log Analytics Workspace 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 Azure Log Analytics Workspace tools.",
)
response = await agent.run("List recent Azure Log Analytics Workspace 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 Azure Log Analytics Workspace. 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 Azure Log Analytics Workspace MCP in LlamaIndex

Install `llama-index-tools-mcp` and configure a `BasicMCPClient`. Convert the server connection into a tool list using `to_tool_list_async()` and pass it to your FunctionAgent.

The server restricts access to one specific table. Your agent provides the KQL filters, but it cannot jump to unauthorized datasets.

No custom code is required. The MCP integration handles the API interaction, returning structured JSON that LlamaIndex can parse and embed natively.

You should instruct your agent to use the `limit` operator in its KQL strings. Bounding the results prevents token overflow during the embedding phase.

The Vinkius MCP endpoint requires a single auth token and runs in an isolated runtime environment. Your raw system logs flow straight to your vector store without lingering on any intermediate disk.

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