How to Use the Kibana MCP in AutoGen

Q: Can AutoGen agents modify Kibana rules?

Yes, they can call createrule or updaterule. You should pair these agents with a human-in-the-loop setting to verify changes.

Q: How do I prevent Kibana rule spam in AutoGen?

You instruct your agents to call findrules before creating new ones. They can compare current rules and decide if an update is needed.

Enable multi-agent debate for Kibana management in AutoGen. Let your security and ops agents negotiate dashboard changes.

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Setup Kibana with AutoGen

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Debate Kibana changes between agents

Assign different roles to your agents to manage your observability setup. One agent might propose a change via `update_saved_object` while another evaluates it for security risks. They reach consensus before execution. This prevents accidental deletions or misconfigurations by requiring a second agent to approve the tool call.

Automate Kibana case management

Use a specialized agent to monitor incidents and create cases. It calls `create_case` whenever it detects an anomaly in your logs. A secondary agent checks the case details using `get_case`. If it finds missing info, it uses `add_case_comment` to request more context from human engineers.

Coordinate multi-space configurations

Handle complex migrations across multiple environments. An orchestrator agent lists available spaces with `list_spaces` and delegates import tasks to worker agents. Worker agents execute `import_saved_objects` for their assigned spaces. If an error occurs, the orchestrator uses `resolve_import_errors` to decide the next step.

Setup guide

Set up Kibana 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 Kibana 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="Kibana_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Kibana 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 Kibana. 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 Kibana MCP in AutoGen

Agents communicate through the conversation loop. When one agent calls `find_saved_objects`, the output becomes available to the rest of the team.

Yes, they can call `create_rule` or `update_rule`. You should pair these agents with a human-in-the-loop setting to verify changes.

You instruct your agents to call `find_rules` before creating new ones. They can compare current rules and decide if an update is needed.

You can set up a validation agent that reviews tool outputs. If it detects a bad state, it can call `delete_space` or `update_space` to revert.

Access is restricted to the credentials provided at the endpoint. Only agents within your defined group have access to the tools that touch your Kibana data.

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