How to integrate Kibana MCP with Autogen

This guide walks you through connecting Kibana to AutoGen using the Composio tool router. By the end, you'll have a working Kibana agent that can visualize weekly sales data as a chart, list top error logs from last 24 hours, generate dashboard of user activity trends through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Kibana account through Composio's Kibana MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Kibana is a visualization and analytics platform for Elasticsearch data. It helps you explore, visualize, and monitor your data using intuitive dashboards and interactive tools.

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Introduction

This guide walks you through connecting Kibana to AutoGen using the Composio tool router. By the end, you'll have a working Kibana agent that can visualize weekly sales data as a chart, list top error logs from last 24 hours, generate dashboard of user activity trends through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Kibana account through Composio's Kibana MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Kibana with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Kibana
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Kibana tools
  • Run a live chat loop where you ask the agent to perform Kibana operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

What is the Kibana MCP server, and what's possible with it?

The Kibana MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Kibana account. It provides structured and secure access so your agent can perform Kibana operations on your behalf.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Kibana account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Kibana via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Kibana connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

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

load_dotenv()

async def main():
    # Initialize Composio and create a Kibana session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["kibana"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Kibana tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Kibana assistant agent with MCP tools
    agent = AssistantAgent(
        name="kibana_assistant",
        description="An AI assistant that helps with Kibana operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Kibana tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Kibana related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Kibana tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Kibana and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

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

load_dotenv()

async def main():
    # Initialize Composio and create a Kibana session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["kibana"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Kibana assistant agent with MCP tools
        agent = AssistantAgent(
            name="kibana_assistant",
            description="An AI assistant that helps with Kibana operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Kibana related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Kibana through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Kibana, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Kibana action and event your agent gets out of the box.

Delete Alerting Rule

Tool to delete an alerting rule in Kibana.

Delete Connector

Tool to delete a connector in Kibana.

Delete Fleet Output

Tool to delete a specific output configuration in Kibana Fleet.

Delete Fleet Proxy

Deletes a Fleet proxy configuration by its unique identifier.

Delete List

Deletes a list.

Delete Osquery Saved Query

Delete a saved Osquery query by its saved object ID.

Delete Saved Object

Tool to delete a saved object in Kibana.

Find Kibana Alerts

Tool to find and/or aggregate detection alerts in Kibana.

Get Action Types

Retrieves all available connector types (actions) in Kibana.

Get Alerting Rules

Tool to retrieve a list of alerting rules in Kibana.

Get Rule Types

Retrieves available rule types (alert types) in Kibana.

Get Cases

Tool to retrieve a list of cases in Kibana.

Get All Connectors

Tool to retrieve a list of all connectors in Kibana.

Get Data Views

Retrieves all data views (formerly known as index patterns) available in Kibana.

Find Detection Engine Rules

Retrieves a paginated list of Kibana detection engine rules with flexible filtering and sorting options.

Get Endpoint List Items

Retrieves Elastic Endpoint exception list items with filtering, pagination, and sorting capabilities.

Get Entity Store Engines

Retrieves all entity store engines configured in Kibana.

List Entity Store Entities

Tool to list entity records in the entity store with support for paging, sorting, and filtering.

Get Entity Store Status

Retrieves the current status of the Kibana Entity Store and its configured engines.

Get Fleet Agent Policies

Retrieves a paginated list of Fleet agent policies with filtering, sorting, and optional detailed information.

Get Fleet Agents Available Versions

Tool to retrieve the available versions for Fleet agents.

Get Fleet Agents Setup Status

Check Fleet setup readiness and identify missing requirements.

Check Fleet Permissions

Tool to check the permissions for the Fleet API.

Get Fleet Enrollment API Key

Tool to retrieve details of a specific enrollment API key by its ID.

Get Fleet Enrollment API Keys

Tool to fetch a list of enrollment API keys.

Get Fleet EPM Categories

Get all available package categories in the Elastic Package Manager (EPM) with package counts.

Get Fleet EPM Data Streams

Tool to retrieve the list of data streams in the Elastic Package Manager.

Get Fleet EPM Package Details

Retrieves comprehensive details for a specific Fleet integration package version from the Elastic Package Manager (EPM).

Get Fleet EPM Package File

Retrieves a specific file from an Elastic Package Manager (EPM) package.

Get Fleet EPM Packages

Tool to fetch the list of available packages in the Elastic Package Manager.

Get Installed EPM Packages

Tool to retrieve the list of installed packages in the Elastic Package Manager.

Get Fleet EPM Packages (Limited)

Retrieves a limited list of package names from the Elastic Package Manager (EPM) registry.

Get EPM Package Statistics

Retrieves usage statistics for a specific Fleet package in Kibana, including the number of package policies and agent policies using the package.

Get Fleet Package Policies

Retrieves a list of Fleet package policies (integration policies) in Kibana.

Get Fleet Server Host

Tool to fetch details of a specific Fleet server host by its item ID.

Get Fleet Server Hosts

Tool to retrieve the list of Fleet Server hosts.

Get Index Management Indices

Tool to fetch information about indices managed by Kibana's Index Management feature.

Get Node Metrics

Tool to retrieve statistics for nodes in an Elasticsearch cluster, often visualized in Kibana.

Get Reporting Jobs

Tool to retrieve a list of reporting jobs in Kibana.

Get Saved Objects

Tool to retrieve a list of saved objects in Kibana based on specified criteria.

Get Kibana Status

Tool to get the current status of Kibana.

Create Alerting Rule

Tool to create a new alerting rule in Kibana.

Create Case

Tool to create a new case in Kibana.

Create Kibana Connector

Tool to create a new connector in Kibana.

Create Dashboard

Tool to create a new dashboard in Kibana.

Create Data View

Tool to create a new data view (index pattern) in Kibana.

Create or Update Saved Object

Tool to create or update a saved object in Kibana.

FRAMEWORKS

How to build Kibana MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Kibana MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Kibana MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Kibana MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Kibana MCP with Claude Cowork

Codex logo

Codex

Use Kibana MCP with Codex

OpenClaw logo

OpenClaw

Use Kibana MCP with OpenClaw

Hermes logo

Hermes

Use Kibana MCP with Hermes

CLI logo

CLI

Use Kibana MCP with CLI

Google ADK logo

Google ADK

Use Kibana MCP with Google ADK

LangChain logo

LangChain

Use Kibana MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Kibana MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Kibana MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Kibana MCP with LlamaIndex

CrewAI logo

CrewAI

Use Kibana MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Kibana MCP server, the agents and LLMs can only access a fixed set of Kibana tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Kibana and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Kibana tools.

Yes, absolutely. You can configure which Kibana scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Kibana data and credentials are handled as safely as possible.

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave
Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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Tomisin Jenrola
Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

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Jerome Leclanche
Co-Founder, Ingram Technologies