How to integrate iLert MCP with Pydantic AI

This guide walks you through connecting iLert to Pydantic AI using the Composio tool router. By the end, you'll have a working iLert agent that can review alerts, inspect your incidents, check on-call coverage, list schedules, and browse resources through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a iLert account through Composio's iLert MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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iLert is an incident response platform for alerts, on-call schedules, escalations, incidents, and status pages. It helps teams route incidents fast, reduce downtime, and keep users informed.

8 Tools

Introduction

This guide walks you through connecting iLert to Pydantic AI using the Composio tool router. By the end, you'll have a working iLert agent that can review alerts, inspect your incidents, check on-call coverage, list schedules, and browse resources through natural language commands.

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

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

Also integrate iLert with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for iLert
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your iLert workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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

The iLert MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your iLert account. It provides structured and secure access to your alerts, incidents, on-call schedules, escalation policies, services, teams, and maintenance windows, so your agent can review alerts, track incidents, identify on-call responders, inspect schedules, and browse response resources on your behalf.

  • Alert monitoring: Have your agent list alerts by state, source, escalation policy, responder, or report time, then retrieve the details of a specific alert.
  • Incident tracking: Let the agent list status incidents by lifecycle state, affected service, or report time, and review a specific incident's affected teams and history.
  • On-call coverage: Direct your agent to find who is on call for selected schedules, escalation policies, users, and time windows.
  • Schedule visibility: Instruct your agent to list on-call schedules and review current shifts, upcoming shifts, or schedule definitions.
  • Response resource lookup: Have your agent browse services, teams, escalation policies, and maintenance windows when investigating operational issues.

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like iLert
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
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

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to iLert
  • MCPServerStreamableHTTP connects to the iLert MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for iLert
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["ilert"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to iLert tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
ilert_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[ilert_mcp],
    instructions=(
        "You are a iLert assistant. Use iLert tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the iLert endpoint
  • The agent uses GPT-5 to interpret user commands and perform iLert operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with iLert.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • iLert API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with iLert and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for iLert
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["ilert"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    ilert_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[ilert_mcp],
        instructions=(
            "You are a iLert assistant. Use iLert tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with iLert.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with iLert through Composio's Tool Router. With this setup, your agent can perform real iLert actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + iLert for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

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

Get Alert

Get one iLert alert by its numeric ID, optionally expanding escalation rules, the next escalation user, or custom details.

Get Current User

Return the authenticated iLert user, including identity, role, timezone, and active team context.

Get Incident

Get one iLert status incident by numeric ID, optionally including subscription state, affected teams, or history.

List Alerts

List iLert alerts with optional state, source, escalation-policy, responder, and report-time filters.

List Incidents

List iLert status incidents with optional lifecycle-state, affected-service, and report-time filters.

List On Calls

Find who is on call for selected escalation policies, schedules, users, and a time window of at most three months.

List Resources

List one incident-response resource catalog: services, teams, escalation policies, or maintenance windows.

List Schedules

List iLert on-call schedules, optionally expanding current or next shifts and schedule definitions.

FRAMEWORKS

How to build iLert MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use iLert MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use iLert MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use iLert MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use iLert MCP with Claude Cowork

Codex logo

Codex

Use iLert MCP with Codex

OpenClaw logo

OpenClaw

Use iLert MCP with OpenClaw

Hermes logo

Hermes

Use iLert MCP with Hermes

CLI logo

CLI

Use iLert MCP with CLI

Google ADK logo

Google ADK

Use iLert MCP with Google ADK

LangChain logo

LangChain

Use iLert MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use iLert MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use iLert MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use iLert MCP with LlamaIndex

CrewAI logo

CrewAI

Use iLert MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Pydantic AI 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 iLert tools.

Yes, absolutely. You can configure which iLert 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 iLert data and credentials are handled as safely as possible.

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