How to Use the Honeycomb MCP in AutoGen

Q: How do Honeycomb and AutoGen agents reach a consensus?

They use tools like runquery to gather facts. Once the data is retrieved, the agents discuss the findings until they agree on the next step.

Q: What if the agents disagree on a query result?

They can run getqueryresult again or refine their createqueryspecification parameters until they reach a result everyone accepts.

Debate your system performance in AutoGen using this MCP Server to fetch and annotate your Honeycomb data.

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Connect Honeycomb MCP to AutoGen

Create your Vinkius account to connect Honeycomb 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 Honeycomb with AutoGen

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Perplexity

Debate incidents with AutoGen agents

Let your agents argue about the data. One agent runs `run_query` to get trace results, while another challenges the interpretation using the schema from `list_dataset_columns`. This prevents hasty decisions. By forcing a debate, you ensure that the conclusion reached is backed by actual telemetry instead of a single agent's hallucination.

Collaborate on marker creation

Your team of agents can decide when to annotate the timeline. A performance agent might suggest a marker, while a security agent reviews the `create_marker` request before execution. It enforces a process. No marker goes up unless the group reaches a consensus, keeping your timeline clean and meaningful.

Negotiate alert thresholds

Use `list_triggers` to show your agents the current system boundaries. They can then debate whether those thresholds are still appropriate given recent performance trends. It keeps your alerts relevant. The agents negotiate based on the data they see, ensuring your team only gets paged for real issues.

Setup guide

Set up Honeycomb 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 Honeycomb 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="Honeycomb_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Honeycomb 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="Honeycomb_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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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 Honeycomb. 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 Honeycomb MCP in AutoGen

They use tools like `run_query` to gather facts. Once the data is retrieved, the agents discuss the findings until they agree on the next step.

They can. An agent proposes a marker, and another agent approves it. Once approved, the MCP server performs the `create_marker` action.

It is. The data is only shared between your agents in the local conversation. No external party has access to your telemetry.

They can run `get_query_result` again or refine their `create_query_specification` parameters until they reach a result everyone accepts.

Yes. You control the permissions of the MCP server. You can allow read-only access for queries while restricting write access for markers.

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