# How to integrate Iqair airvisual MCP with Pydantic AI ```json { "title": "How to integrate Iqair airvisual MCP with Pydantic AI", "toolkit": "Iqair airvisual", "toolkit_slug": "iqair_airvisual", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/iqair_airvisual/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/iqair_airvisual/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:16:23.019Z" } ``` ## Introduction This guide walks you through connecting Iqair airvisual to Pydantic AI using the Composio tool router. By the end, you'll have a working Iqair airvisual agent that can show today's air quality in los angeles, list cities in maharashtra, india with data, get historical aqi for paris last week through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Iqair airvisual account through Composio's Iqair airvisual MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Iqair airvisual with - [OpenAI Agents SDK](https://composio.dev/toolkits/iqair_airvisual/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/iqair_airvisual/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/iqair_airvisual/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/iqair_airvisual/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/iqair_airvisual/framework/codex) - [OpenClaw](https://composio.dev/toolkits/iqair_airvisual/framework/openclaw) - [Hermes](https://composio.dev/toolkits/iqair_airvisual/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/iqair_airvisual/framework/cli) - [Google ADK](https://composio.dev/toolkits/iqair_airvisual/framework/google-adk) - [LangChain](https://composio.dev/toolkits/iqair_airvisual/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/iqair_airvisual/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/iqair_airvisual/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/iqair_airvisual/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/iqair_airvisual/framework/crew-ai) ## 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 Iqair airvisual - 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 Iqair airvisual 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 Iqair airvisual MCP server, and what's possible with it? The Iqair airvisual MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Iqair airvisual account. It provides structured and secure access to rich global air quality data, so your agent can perform actions like retrieving real-time AQI, forecasting pollution, checking historical trends, and ranking cities worldwide by air quality on your behalf. - Real-time city and station air quality: Instantly fetch current air quality and weather data for any supported city or monitoring station, based on precise location or station ID. - Air quality forecasting: Ask your agent to provide air quality forecasts for specific cities, helping you plan activities based on pollution trends. - Historical AQI analysis: Retrieve historical air quality index readings for cities to analyze patterns, spot trends, or track improvements over time. - World AQI rankings: Get live rankings of cities worldwide based on current AQI data, or easily find the most and least polluted cities globally. - Location-based discovery: Let your agent list supported countries, states, and cities, or pinpoint the nearest air quality stations and cities using GPS coordinates or IP address. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `IQAIR_AIRVISUAL_GET_AIR_QUALITY_FORECAST_DATA` | Get Air Quality Forecast Data | Tool to retrieve air quality forecast data for a specified city, state, and country. Use after confirming location details. | | `IQAIR_AIRVISUAL_GET_CITIES` | Get Cities | Tool to list supported cities in a specified state and country. Use when you need to retrieve cities for a given state/country. | | `IQAIR_AIRVISUAL_GET_CITY_AIR_QUALITY` | Get City Air Quality | Tool to retrieve air quality data for a specific city. Use when you need current pollution and weather data by specifying city, state, and country. | | `IQAIR_AIRVISUAL_GET_COUNTRIES` | Get supported countries | Tool to list all supported countries. Use when you need to know which countries are supported by the AirVisual API. | | `IQAIR_AIRVISUAL_GET_HISTORICAL_AQI_DATA` | Get Historical AQI Data | Tool to retrieve historical air quality data for a city. Use after confirming city, state, and country when you need AQI readings over time. | | `IQAIR_AIRVISUAL_GET_NEAREST_CITY_AIR_QUALITY` | Get Nearest City Air Quality | Tool to retrieve air quality data for the nearest city based on latitude/longitude or IP. Use when you have precise location data or want to geolocate an IP for air quality. | | `IQAIR_AIRVISUAL_GET_NEAREST_STATION_AIR_QUALITY` | Get Nearest Station Air Quality | Tool to get nearest station air quality. Use when you have GPS coordinates and need closest station’s AQI. | | `IQAIR_AIRVISUAL_GET_STATES` | Get States | Tool to list supported states in a specified country. Use when you need to retrieve states/provinces for a given country. | | `IQAIR_AIRVISUAL_GET_STATION_BY_ID` | Get Station by ID | Fetches current air quality and weather data for a specific location by station/city/state/country names. Falls back to city-level data if the Stations API requires premium access. Returns current AQI (US and China scales), weather conditions, and geographic location. Use Get Countries, Get States, and Get Cities actions to find valid location names. | | `IQAIR_AIRVISUAL_GET_WORLD_RANKING` | Get World AQI Rankings | Retrieves air quality ranking data. With premium API access, returns a global ranking of cities by AQI. With standard (free) API access, automatically falls back to returning the nearest city's air quality as a single-item ranking list. Use lat/lon coordinates or an IP address to specify location for the fallback behavior. Returns city name, location, and current AQI values (US EPA and Chinese MEP standards). | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Iqair airvisual MCP server is an implementation of the Model Context Protocol that connects your AI agent to Iqair airvisual. It provides structured and secure access so your agent can perform Iqair airvisual operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 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 ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install the required libraries. What's happening: - composio connects your agent to external SaaS tools like Iqair airvisual - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables 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 ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Iqair airvisual - MCPServerStreamableHTTP connects to the Iqair airvisual MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```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() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Iqair airvisual 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 ```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 Iqair airvisual composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["iqair_airvisual"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Iqair airvisual endpoint - The agent uses GPT-5 to interpret user commands and perform Iqair airvisual operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent iqair_airvisual_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[iqair_airvisual_mcp], instructions=( "You are a Iqair airvisual assistant. Use Iqair airvisual tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Iqair airvisual API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```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 Iqair airvisual.\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() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```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 Iqair airvisual composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["iqair_airvisual"], ) 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 iqair_airvisual_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[iqair_airvisual_mcp], instructions=( "You are a Iqair airvisual assistant. Use Iqair airvisual 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 Iqair airvisual.\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 Iqair airvisual through Composio's Tool Router. With this setup, your agent can perform real Iqair airvisual 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 + Iqair airvisual for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## How to build Iqair airvisual MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/iqair_airvisual/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/iqair_airvisual/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/iqair_airvisual/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/iqair_airvisual/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/iqair_airvisual/framework/codex) - [OpenClaw](https://composio.dev/toolkits/iqair_airvisual/framework/openclaw) - [Hermes](https://composio.dev/toolkits/iqair_airvisual/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/iqair_airvisual/framework/cli) - [Google ADK](https://composio.dev/toolkits/iqair_airvisual/framework/google-adk) - [LangChain](https://composio.dev/toolkits/iqair_airvisual/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/iqair_airvisual/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/iqair_airvisual/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/iqair_airvisual/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/iqair_airvisual/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Iqair airvisual MCP? With a standalone Iqair airvisual MCP server, the agents and LLMs can only access a fixed set of Iqair airvisual tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Iqair airvisual and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Pydantic AI? 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 Iqair airvisual tools. ### Can I manage the permissions and scopes for Iqair airvisual while using Tool Router? Yes, absolutely. You can configure which Iqair airvisual 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 Iqair airvisual data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)