How to integrate Kibana MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Kibana to the OpenAI Agents SDK 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 OpenAI Agents SDK 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 and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Kibana
  • Configure an AI agent that can use Kibana as a tool
  • Run a live chat session where you can ask the agent to perform Kibana operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Kibana project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Kibana.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Kibana Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["kibana"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only kibana.
  • The router checks the user's Kibana connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Kibana.
  • This approach keeps things lightweight and lets the agent request Kibana tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Kibana. "
        "Help users perform Kibana operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Kibana and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Kibana operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Kibana.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Kibana and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["kibana"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Kibana. "
        "Help users perform Kibana operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Kibana MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Kibana.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

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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 OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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.

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