How to integrate Kibana MCP with CrewAI

GET STARTED FOR FREE
Framework Integration Gradient
Kibana Logo
CrewAI Logo
divider

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.

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 so your agent can perform Kibana operations on your behalf.

Supported Tools & Triggers

Tools
Delete ActionTool to delete an action in kibana.
Delete Alerting RuleTool to delete an alerting rule in kibana.
Delete ConnectorTool to delete a connector in kibana.
Delete Fleet OutputTool to delete a specific output configuration in kibana fleet.
Delete Fleet ProxyTool to delete a specific fleet proxy configuration by its id.
Delete ListDeletes a list.
Delete Osquery Saved QueryTool to delete a saved osquery query by its id.
Delete Saved ObjectTool to delete a saved object in kibana.
Find Kibana AlertsTool to find and/or aggregate detection alerts in kibana.
Get Action TypesTool to fetch the list of available action types (e.
Get Alerting RulesTool to retrieve a list of alerting rules in kibana.
Get Alert TypesTool to retrieve available alert types in kibana.
Get CasesTool to retrieve a list of cases in kibana.
Get All ConnectorsTool to retrieve a list of all connectors in kibana.
Get Data ViewsTool to retrieve a list of data views available in kibana.
Find Detection Engine RulesRetrieves a list of detection engine rules based on specified criteria.
Get Endpoint List ItemsTool to retrieve all items from an endpoint exception list.
Get Entity Store EnginesRetrieves the list of engines from the entity store.
List Entity Store EntitiesTool to list entity records in the entity store with support for paging, sorting, and filtering.
Get Entity Store StatusTool to retrieve the status of the entity store in kibana.
Get Fleet Agent PoliciesFetches a list of agent policies in fleet.
Get Fleet Agents Available VersionsTool to retrieve the available versions for fleet agents.
Get Fleet Agents Setup StatusTool to check if the fleet agents are set up.
Check Fleet PermissionsTool to check the permissions for the fleet api.
Get Fleet Data StreamsRetrieves the list of data streams in fleet.
Get Fleet Enrollment API KeyTool to retrieve details of a specific enrollment api key by its id.
Get Fleet Enrollment API KeysTool to fetch a list of enrollment api keys.
Get Fleet EPM CategoriesTool to fetch the list of categories in the elastic package manager.
Get Fleet EPM Data StreamsTool to retrieve the list of data streams in the elastic package manager.
Get Fleet EPM Package DetailsTool to fetch details of a specific package and version in the elastic package manager (epm).
Get Fleet EPM Package FileTool to retrieve a specific file from a package in the elastic package manager.
Get Fleet EPM PackagesTool to fetch the list of available packages in the elastic package manager.
Get Installed EPM PackagesTool to retrieve the list of installed packages in the elastic package manager.
Get Fleet EPM Packages (Limited)Tool to fetch a limited list of packages from the elastic package manager.
Get EPM Package StatisticsTool to retrieve statistics for a specific package in the elastic package manager.
Get Fleet Package PoliciesTool to retrieve a list of all package policies (agent & epm), providing their ids and associated details.
Get Fleet Server HostTool to fetch details of a specific fleet server host by its item id.
Get Fleet Server HostsTool to retrieve the list of fleet server hosts.
Get Index Management IndicesTool to fetch information about indices managed by kibana's index management feature.
Get Node MetricsTool to retrieve statistics for nodes in an elasticsearch cluster, often visualized in kibana.
Get Reporting JobsTool to retrieve a list of reporting jobs in kibana.
Get Saved ObjectsTool to retrieve a list of saved objects in kibana based on specified criteria.
Get Kibana StatusTool to get the current status of kibana.
Create Alerting RuleTool to create a new alerting rule in kibana.
Create CaseTool to create a new case in kibana.
Create Kibana ConnectorTool to create a new connector in kibana.
Create DashboardTool to create a new dashboard in kibana.
Create Data ViewTool to create a new data view (index pattern) in kibana.
Create or Update Saved ObjectTool to create or update a saved object in kibana.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

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

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.

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

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

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

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

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.

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

How to build Kibana MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Kibana MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Kibana MCP with Claude Agent SDK

Google ADK

Google ADK

Use Kibana MCP with Google ADK

LangChain

LangChain

Use Kibana MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Kibana MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Kibana MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Kibana MCP with LlamaIndex

Claude Code

Claude Code

Use Kibana MCP with Claude Code

Codex

Codex

Use Kibana MCP with Codex

FAQ

What are the differences in Tool Router MCP and Kibana MCP?

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.

Can I use Tool Router MCP with CrewAI?

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.

Can I manage the permissions and scopes for Kibana while using Tool Router?

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.

How safe is my data with Composio Tool Router?

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.

Used by agents from

Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free