How to integrate Adafruit IO MCP with CrewAI

This guide walks you through connecting Adafruit IO to CrewAI 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 CrewAI 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 CrewAI 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 CrewAI 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 AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Adafruit IO connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Adafruit IO
  • Build a conversational loop where your agent can execute Adafruit IO 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 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 step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Adafruit IO connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
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 crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Adafruit IO via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
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_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
5

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 Adafruit IO MCP URL
6

Create a Composio Tool Router session for Adafruit IO

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["adafruit_io"])

url = session.mcp.url
What's happening:
  • You create a Adafruit IO only session through Composio
  • Composio returns an MCP HTTP URL that exposes Adafruit IO tools
7

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.
8

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 Adafruit IO 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=["adafruit_io"],
)
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 Adafruit IO through Composio's Tool Router. The agent can perform Adafruit IO 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
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

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. 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 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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