How to integrate Kibana MCP with CrewAI

This guide walks you through connecting Kibana to CrewAI 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 CrewAI 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 CrewAI 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 CrewAI 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 AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Kibana connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Kibana
  • Build a conversational loop where your agent can execute Kibana operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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 to your alerts, cases, data views, saved content, Fleet setup, and monitoring data, so your agent can investigate alerts, review configurations, check system status, inspect integrations, and remove outdated resources on your behalf.

  • Alert and security investigation: Have your agent find detection alerts, review alerting and detection rules, inspect security cases, and examine endpoint exceptions.
  • Fleet and integration oversight: Let the agent review agent policies, package policies, enrollment keys, server hosts, installed integrations, available packages, and Fleet setup readiness.
  • Data and content discovery: Direct your agent to list data views, inspect saved dashboards and other saved content, and check reporting jobs.
  • Cluster and index monitoring: Instruct your agent to review Elasticsearch node metrics, inspect managed indices, and check entity store engines, records, and status.
  • Configuration cleanup: Have your agent review connectors and supported action types, then delete obsolete alerting rules, connectors, saved content, lists, Fleet outputs, Fleet proxies, or saved Osquery queries.

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Kibana connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
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 crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Kibana via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Kibana MCP URL
6

Create a Composio Tool Router session for Kibana

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["kibana"])

url = session.mcp.url
What's happening:
  • You create a Kibana only session through Composio
  • Composio returns an MCP HTTP URL that exposes Kibana tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["kibana"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Kibana through Composio's Tool Router. The agent can perform Kibana operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
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

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. CrewAI 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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