How to integrate Ntfy MCP with Pydantic AI

This guide walks you through connecting Ntfy to Pydantic AI using the Composio tool router. By the end, you'll have a working Ntfy agent that can send push notification for build failures, notify me of high-priority alerts, broadcast message to all devices through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Ntfy account through Composio's Ntfy MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Ntfy is a notification service to send push messages to phones or desktops. Instantly deliver alerts and updates to users, devices, or teams.

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Introduction

This guide walks you through connecting Ntfy to Pydantic AI using the Composio tool router. By the end, you'll have a working Ntfy agent that can send push notification for build failures, notify me of high-priority alerts, broadcast message to all devices through natural language commands.

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

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

Also integrate Ntfy with

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle 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 Ntfy
  • 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 Ntfy 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 Ntfy MCP server, and what's possible with it?

The Ntfy MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ntfy account. It provides structured and secure access to your push notifications, topics, subscriptions, account details, and service status, so your agent can publish notifications, review scheduled messages, monitor topic streams, manage browser subscriptions, and check service health on your behalf.

  • Push notification publishing: Have your agent publish messages to Ntfy topics for delivery to your phone or desktop.
  • Topic monitoring: Let the agent subscribe to a topic as a text stream and review messages scheduled for later delivery.
  • Browser notification management: Direct your agent to create or remove web push subscriptions for browser notifications.
  • Account and service insights: Instruct your agent to retrieve account details, check service health, review server statistics, or compare available service tiers.
  • Attachment inspection: Have your agent check a message attachment's metadata without downloading its contents.

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 Ntfy
  • 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 Ntfy
  • MCPServerStreamableHTTP connects to the Ntfy 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 Ntfy
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["ntfy"],
    )
    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 Ntfy 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
ntfy_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[ntfy_mcp],
    instructions=(
        "You are a Ntfy assistant. Use Ntfy tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Ntfy endpoint
  • The agent uses GPT-5 to interpret user commands and perform Ntfy 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 Ntfy.\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
  • Ntfy 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 Ntfy 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 Ntfy
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["ntfy"],
    )
    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
    ntfy_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[ntfy_mcp],
        instructions=(
            "You are a Ntfy assistant. Use Ntfy 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 Ntfy.\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 Ntfy through Composio's Tool Router. With this setup, your agent can perform real Ntfy 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 + Ntfy 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 Ntfy action and event your agent gets out of the box.

Create NTFY Account

Tool to register a new user account on ntfy.

Create Web Push Subscription

Tool to register a web push subscription for browser notifications.

Delete Web Push Subscription

Tool to unregister a web push subscription from the ntfy server.

Fetch Cached Messages

Tool to fetch cached messages from a ntfy topic.

Fetch Latest Message from Topic

Tool to fetch the most recent message from a topic's cache.

Fetch Scheduled Messages

Tool to fetch messages scheduled for later delivery from a topic.

Get Account Information

Tool to retrieve account data for authenticated user or anonymous user.

Get Server Statistics

Tool to retrieve server statistics including message counts and publishing rates.

Get Service Tiers

Tool to list all available ntfy service tiers with their limits and features.

Get File Attachment Metadata

Tool to get file attachment metadata from a message without downloading the file content.

Check NTFY Service Health

Tool to check the health status of the ntfy service.

Poll Messages from Topic

Tool to poll for messages from an ntfy topic without maintaining a long-standing connection.

Publish Message as JSON to NTFY

Tool to publish messages as JSON to ntfy.

Publish Message to Topic

Tool to publish a message to a ntfy topic.

Publish Message to Topic (PUT)

Tool to publish a message to a topic using PUT method.

Publish Message via GET

Tool to publish messages to ntfy via GET request with URL parameters.

Send Message via Webhook

Tool to send messages via webhook endpoint using simple GET request.

Subscribe to NTFY Topic with Filters

Tool to subscribe to a ntfy topic with filters based on message fields (id, message, title, priority, tags).

Subscribe to Topic (JSON Stream)

Tool to subscribe to a ntfy topic and receive messages as JSON stream.

Subscribe to Multiple NTFY Topics

Tool to subscribe to multiple ntfy topics simultaneously using comma-separated topic list.

Subscribe to Topic (Raw Stream)

Tool to subscribe to a topic and receive message bodies as raw text stream.

Trigger NTFY Webhook

Tool to trigger a webhook to publish a message to an ntfy topic via simple HTTP GET request.

FRAMEWORKS

How to build Ntfy MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Ntfy MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Ntfy MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Ntfy MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Ntfy MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Ntfy MCP with Claude Cowork

Codex logo

Codex

Use Ntfy MCP with Codex

Cursor logo

Cursor

Use Ntfy MCP with Cursor

OpenClaw logo

OpenClaw

Use Ntfy MCP with OpenClaw

Hermes logo

Hermes

Use Ntfy MCP with Hermes

CLI logo

CLI

Use Ntfy MCP with CLI

Google ADK logo

Google ADK

Use Ntfy MCP with Google ADK

LangChain logo

LangChain

Use Ntfy MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Ntfy MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Ntfy MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Ntfy MCP with LlamaIndex

CrewAI logo

CrewAI

Use Ntfy MCP with CrewAI

OpenCode logo

OpenCode

Use Ntfy MCP with OpenCode

VS Code logo

VS Code

Use Ntfy MCP with VS Code

FAQ

Frequently asked questions

With a standalone Ntfy MCP server, the agents and LLMs can only access a fixed set of Ntfy tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Ntfy 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 Ntfy tools.

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

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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.

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