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

Q: Can AutoGen agents debate HyperDX (Open Source Observability) logs?

Yes. By providing the listlogs tool to your agents, they can share log data and debate the meaning of specific error patterns during their conversation.

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Connect HyperDX (Open Source Observability) MCP to AutoGen

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

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Collaborative log investigation

Task a group of AutoGen agents to analyze logs and reach a consensus on incident root causes. Use `list_logs` to feed raw error data into the conversation for your agents to debate. This prevents single-agent bias. One agent can focus on log parsing while another verifies the findings against known system patterns.

Autonomous alert negotiation

Let your agents debate whether to adjust an alert threshold. A performance agent can propose a change via `create_alert`, while a security agent reviews the rule against established policies. This setup ensures that no alert is modified without internal scrutiny. The agents negotiate until they reach a decision that balances monitoring sensitivity with system stability.

Multi-agent dashboard inspection

Use multiple agents to interpret complex dashboard telemetry. Agents can call `get_dashboard` to pull specific metrics and then cross-examine the data to identify bottlenecks. This mimics a team of engineers working together. They can highlight specific dashboard panels that correlate with ongoing events, providing you with a summarized, verified conclusion.

Setup guide

Set up HyperDX (Open Source Observability) 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 HyperDX (Open Source Observability) 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="HyperDX (Open Source Observability)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent HyperDX (Open Source Observability) 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="HyperDX (Open Source Observability)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent HyperDX (Open Source Observability) 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 HyperDX. 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 HyperDX (Open Source Observability) MCP in AutoGen

Yes. By providing the `list_logs` tool to your agents, they can share log data and debate the meaning of specific error patterns during their conversation.

Agents can propose, review, and execute alert changes using `create_alert` and `delete_alert`. The conversation framework ensures these actions are discussed before execution.

It allows for multi-perspective analysis. One agent can look for latency issues while another checks for error rates, ensuring a thorough review of the dashboard data.

The AutoGen framework requires agents to agree on an action. You can define a protocol where an 'admin' agent provides the final approval for any `delete_alert` command.

The server uses a single endpoint token for all operations. All alert modifications are logged by the system, ensuring an audit trail for every action taken by the agents.

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