How to integrate Adafruit IO MCP with Pydantic AI

This guide walks you through connecting Adafruit IO to Pydantic AI using the Composio tool router. By the end, you'll have a working Adafruit IO agent that can summarize temperature feed anomalies today, publish humidity reading to greenhouse feed, list recent door sensor events through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Adafruit IO account through Composio's Adafruit IO MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Adafruit IO is Adafruit's cloud platform for connected device and IoT project data. It helps you store, visualize, and act on sensor feeds without running your own backend.

21 Tools

Introduction

This guide walks you through connecting Adafruit IO to Pydantic AI using the Composio tool router. By the end, you'll have a working Adafruit IO agent that can summarize temperature feed anomalies today, publish humidity reading to greenhouse feed, list recent door sensor events through natural language commands.

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

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

Also integrate Adafruit IO 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 Adafruit IO
  • 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 Adafruit IO 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 Adafruit IO MCP server, and what's possible with it?

The Adafruit IO MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Adafruit IO account. It provides structured and secure access to your feeds, device data, groups, dashboards, and account status, so your agent can publish readings, review data, organize feeds, manage dashboards, and monitor usage on your behalf.

  • Feed creation and management: Have your agent create, review, update, or delete feeds used to store readings from your connected devices.
  • Device data publishing and review: Let the agent publish timestamped values, inspect recent feed data, and retrieve, correct, or remove specific readings.
  • Feed organization with groups: Direct your agent to create and update groups, add or remove feeds, and review how your device feeds are organized.
  • Dashboard management: Instruct your agent to create dashboards for feed data, review existing dashboards and their blocks, update dashboard details, or remove dashboards.
  • Account and usage monitoring: Have your agent check your Adafruit IO account identity, plan limits, and current data write usage before publishing more readings.

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

Add Feeds to Group

Sequentially add 1-10 existing feeds to one group using one documented provider request per feed.

Create Dashboard

Create a dashboard for visualizing and controlling feed data.

Create Feed

Create a feed, optionally within an existing group.

Create Group

Create a group for organizing feeds.

Delete Dashboard

Permanently delete one dashboard by exact key.

Delete Data Point

Permanently delete one feed data point by ID.

Delete Data Points

Permanently delete 1-30 feed data points sequentially through Adafruit IO's single-data-point DELETE endpoint, bounded by the Free-plan limit of 30 data mutations per minute.

Delete Feed

Permanently delete one feed and its retained data by exact feed key.

Delete Group

Permanently delete one group by exact key.

Get Account Status

Return compact identity, plan limits, and current data-write throttle usage for the connected Adafruit IO account.

Get Dashboards

List dashboards, or retrieve one dashboard and its blocks by key.

Get Data Point

Retrieve one feed data point by its exact ID.

Get Feeds

List feeds, or retrieve one feed by key with optional data summary details.

Get Groups

List groups with feed summaries, or retrieve one group by key.

List Feed Data

Return one newest-first page of feed data with an opaque cursor for the next older page.

Publish Feed Data

Publish one or multiple timestamped values to a feed, using the batch endpoint only when multiple points are supplied.

Remove Feeds from Group

Sequentially remove 1-10 feeds from one group using one documented provider request per feed.

Update Dashboard

Update an existing dashboard by exact dashboard key.

Update Data Point

Replace the value and optional metadata of one existing feed data point.

Update Feed

Update an existing feed by exact feed key.

Update Group

Update an existing group by exact group key.

FRAMEWORKS

How to build Adafruit IO MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Adafruit IO MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Adafruit IO MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Adafruit IO MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Adafruit IO MCP with Claude Cowork

Codex logo

Codex

Use Adafruit IO MCP with Codex

OpenClaw logo

OpenClaw

Use Adafruit IO MCP with OpenClaw

Hermes logo

Hermes

Use Adafruit IO MCP with Hermes

CLI logo

CLI

Use Adafruit IO MCP with CLI

Google ADK logo

Google ADK

Use Adafruit IO MCP with Google ADK

LangChain logo

LangChain

Use Adafruit IO MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Adafruit IO MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Adafruit IO MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Adafruit IO MCP with LlamaIndex

CrewAI logo

CrewAI

Use Adafruit IO MCP with CrewAI

FAQ

Frequently asked questions

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

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

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