How to Use the PurpleAir MCP in Pydantic AI

Q: How do I list sensors using Pydantic AI?

Call the listsensors tool through your MCP toolset. You can pass specific filter parameters to ensure the result set is small enough for your Pydantic AI agent to process efficiently.

Strictly type-safe air quality monitoring for Pydantic AI agents using the PurpleAir MCP server.

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Connect PurpleAir MCP to Pydantic AI

Create your Vinkius account to connect PurpleAir to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup PurpleAir with Pydantic AI

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Claude

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Validated sensor data for Pydantic AI

Every response from `get_sensor_data` is validated against your schema, ensuring your Pydantic AI agent only processes clean data. If the sensor returns malformed values, the agent catches the error immediately. This eliminates silent failures during critical air quality monitoring tasks. You get high-confidence ingestion of PM2.5, humidity, and temperature readings every time the tool runs.

Type-safe location lookups in Pydantic AI

Use `get_sensors_near_me` to find hardware and verify the output structure with Pydantic models. Your Pydantic AI agent maintains total control over the data shapes it receives from the field. This approach prevents hallucinated fields in your agent's reasoning. You define the exact structure expected from the sensor network before the query is ever executed.

Structured historical logs for Pydantic AI

Retrieve time-series data via `get_sensor_history` and let Pydantic AI handle the validation of timestamps and measurements. It creates a robust bridge between raw sensor history and your application logic. For bulk data, `get_sensor_history_csv` provides a predictable stream for your data processing models. It keeps your Pydantic AI agent's state consistent throughout long-running analysis cycles.

Setup guide

Set up PurpleAir MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "purpleair-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to PurpleAir tools.",
)

result = await agent.run("List recent PurpleAir transactions")
print(result.output)

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Common questions about PurpleAir MCP in Pydantic AI

The MCP server sends JSON responses that match your Pydantic models. If the data structure deviates from your expected format, the framework raises a validation error immediately.

You connect the MCP server via an HTTP transport. Your Pydantic AI agent then utilizes the MCPToolset to invoke tools and enforce strict type checking on the inputs and outputs.

It provides superior correctness. If your PurpleAir sensor returns unexpected data, your Pydantic AI agent fails loudly instead of proceeding with corrupt information.

Call the `list_sensors` tool through your MCP toolset. You can pass specific filter parameters to ensure the result set is small enough for your Pydantic AI agent to process efficiently.

The system strictly interacts with public environmental telemetry. No private user records are ever touched by the Pydantic AI toolset, ensuring the data flow remains limited to air quality metrics.

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