How do I find my QWeather API Key?

Log in to the [QWeather Developer Console](https://dev.qweather.com/), create a project, and your API Key will be displayed in the project settings. Ensure you select the correct key type (Web API).

What is the difference between dev and commercial plans?

The 'dev' plan uses `devapi.qweather.com` and has limited data frequency and locations. Commercial plans (standard/premium) use `api.qweather.com` and provide higher resolution data and broader global coverage. Set your `qweather_plan` in the credentials to use the correct endpoint.

Can I query weather by coordinates?

Yes! You can pass coordinates in the format `longitude,latitude` (e.g., '116.41,39.92') to any tool that accepts a `location` parameter.

QWeather / 和风天气 MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: QWeather / 和风天气
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect QWeather / 和风天气 through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to QWeather / 和风天气 "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in QWeather / 和风天气?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About QWeather / 和风天气 MCP Server

Empower your AI agent to orchestrate your daily planning and environmental monitoring with QWeather (和风天气), the premier commercial weather platform in China. By connecting QWeather to your agent, you transform complex meteorological data and location-based environmental searches into a natural conversation. Your agent can instantly retrieve real-time weather, 15-day forecasts, air quality indices, severe weather warnings, and astronomical data without you ever needing to navigate a technical dashboard. Whether you are planning outdoor operations or auditing air quality across different regions, your agent acts as a real-time environmental consultant, providing accurate and fast results from a single, unified source.

Pydantic AI validates every QWeather / 和风天气 tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Weather Orchestration — Retrieve current weather and detailed forecasts (up to 15 days) for any location worldwide.
Air Quality Auditing — Monitor real-time AQI, PM2.5, and PM10 levels to ensure safe operating conditions.
Life Index Insights — Access specialized indices for UV radiation, clothing recommendations, and car washing suitability.
Warning Monitoring — Audit active severe weather warnings to maintain safety and organizational continuity.
Geographic Discovery — Search for location IDs and coordinates using keywords to refine your regional tracking.

The QWeather / 和风天气 MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect QWeather / 和风天气 to Pydantic AI via MCP

Follow these steps to integrate the QWeather / 和风天气 MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from QWeather / 和风天气 with type-safe schemas

Why Use Pydantic AI with the QWeather / 和风天气 MCP Server

Pydantic AI provides unique advantages when paired with QWeather / 和风天气 through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your QWeather / 和风天气 integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your QWeather / 和风天气 connection logic from agent behavior for testable, maintainable code

QWeather / 和风天气 + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the QWeather / 和风天气 MCP Server delivers measurable value.

Type-safe data pipelines: query QWeather / 和风天气 with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple QWeather / 和风天气 tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query QWeather / 和风天气 and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock QWeather / 和风天气 responses and write comprehensive agent tests

QWeather / 和风天气 MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect QWeather / 和风天气 to Pydantic AI via MCP:

get_air_now

5, etc.). Get current air quality

get_indices

Get daily life indices

get_moon_astronomy

Get moonrise and moonset times

get_sun_astronomy

Get sunrise and sunset times

get_warning

Get weather warnings

get_weather_24h

Get 24-hour weather forecast

get_weather_3d

Get 3-day weather forecast

get_weather_7d

Get 7-day weather forecast

get_weather_now

Get current weather

lookup_location

Search for location ID

Example Prompts for QWeather / 和风天气 in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with QWeather / 和风天气 immediately.

"What is the current weather in Beijing (101010100)?"

"Check the air quality for Shanghai today."

"Find the location ID for 'Hangzhou'."

Troubleshooting QWeather / 和风天气 MCP Server with Pydantic AI

Common issues when connecting QWeather / 和风天气 to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

QWeather / 和风天气 + Pydantic AI FAQ

Common questions about integrating QWeather / 和风天气 MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your QWeather / 和风天气 MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.