How to Use the Axiom MCP in AutoGen

Let your AutoGen agents debate and execute Axiom observability tasks.

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Setup Axiom with AutoGen

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Debate Axiom data in AutoGen

Create a team of agents that challenge each other on log analysis. One agent uses `run_query` to find errors, while another checks if the data matches known patterns. They negotiate the findings before presenting a final report. This approach prevents false positives in your observability pipeline.

Manage Axiom dashboards in AutoGen

Have your agents collaborate on your monitoring setup. Use `list_dashboards` to see what exists, then let an agent call `create_dashboard` to suggest a better layout. Your team of agents discusses the best view for your current incident. They ensure your dashboards stay relevant to the team's needs.

Streamline Axiom alerting via AutoGen

Set up a specialized agent to handle your notification logic. It can use `create_notifier` to route alerts and `update_notifier` to refine them based on agent consensus. This removes the burden of manual configuration. The agents ensure your alerts reach the right people at the right time.

Setup guide

Set up Axiom 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 Axiom 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="Axiom_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Axiom data")
print(result.messages[-1].content)

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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="Axiom_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Axiom data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Axiom MCP in AutoGen

The agents share the tool list and discuss which one to invoke. They agree on the parameters before executing the action against Axiom.

Yes. Multiple agents can run different queries on the same dataset. They then compare the Axiom results to reach a firm conclusion.

Not at all. The MCP adapter handles the schema conversion, so your agents can start calling Axiom functions immediately.

Yes. The server is stateless, allowing multiple agents to query and update your Axiom datasets concurrently.

We use ephemeral transport sessions for every call. Your observability logs are never cached by the server, keeping your sensitive system data private.

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