How to Use the Azure Log Analytics Workspace MCP in AutoGen

Q: How do I connect AutoGen to the Azure Log Analytics Workspace MCP?

Install autogen-extmcp. Use mcpservertools with your Vinkius HTTP endpoint, then pass the returned tools directly to your agent.

Let your AutoGen agents debate infrastructure issues using live MCP data from Azure Log Analytics.

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Connect Azure Log Analytics Workspace MCP to AutoGen

Create your Vinkius account to connect Azure Log Analytics Workspace to AutoGen 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 AutoGen

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Live Telemetry in AutoGen

Giving your agents the `query_logs` tool lets them pull actual network data from your Azure Log Analytics Workspace MCP Server to argue over the facts. Your security agent sees a threat, but your ops agent thinks it is just a traffic spike. They fetch the logs to prove their point. One agent writes the KQL to find failed logins, while another adjusts the time window to check historical baselines. They iterate on the data until they reach a consensus on what is actually happening in your environment.

Sandboxed Kusto Operations

Autonomous agents cannot run wild across your database since the `query_logs` tool forces them to operate within a single authorized table. They only supply the pipeline operations, such as `| where Level == 'Error'`. They do not touch the table schemas. The engine automatically prefixes the correct table name before execution. Your AutoGen setup stays secure because the agents physically cannot query unauthorized namespaces.

Instant Tool Distribution

Distributing the `query_logs` tool to your conversation group happens instantly without building custom REST clients. You just pass the Vinkius MCP Server URL into `mcp_server_tools`, and the `McpToolAdapter` handles the schema conversion. It is ready to use immediately. You hand the resulting tool list to your AssistantAgent constructor. The agents understand the required KQL parameters immediately and start pulling telemetry without any manual prompt engineering.

Setup guide

Set up Azure Log Analytics Workspace MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Azure Log Analytics Workspace tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Azure Log Analytics Workspace_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Azure Log Analytics Workspace data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Azure Log Analytics Workspace_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Azure Log Analytics Workspace data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

Install `autogen-ext[mcp]`. Use `mcp_server_tools` with your Vinkius HTTP endpoint, then pass the returned tools directly to your agent.

Yes. Once you load the tools, you can assign them to any specific agent in your conversational group. Usually, you give this to a dedicated metrics persona.

No. The server configuration handles the table binding. The agent only needs to generate the Kusto query logic starting with the pipe character.

This is a read-only integration. The worst an agent can do is write an invalid KQL string, which simply returns a syntax error for them to fix and retry.

Security is built into the architecture. The Vinkius container runs in a stateless sandbox, ensuring your diagnostic telemetry routes safely to your local machine without being cached.

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