How to Use the Caiyun Weather / 彩云天气 MCP in Pydantic AI

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Connect Caiyun Weather / 彩云天气 MCP to Pydantic AI

Create your Vinkius account to connect Caiyun Weather / 彩云天气 to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Caiyun Weather / 彩云天气 with Pydantic AI

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Perplexity

Strict runtime schema validation for weather

When your agent calls `get_realtime_weather` or `get_daily_forecast`, this MCP Server returns structured data that Pydantic AI validates instantly. Stop worrying about silent API failures or broken JSON payloads. If the weather service returns unexpected formatting, your agent raises a validation error immediately. This keeps your production systems clean and prevents corrupted data from propagating.

Minute-precision rain data in Pydantic AI

The tools `get_minutely_rain` and `get_precipitation_probability` let your agent check exactly when and where rain will fall with absolute type safety. You can make critical dispatch decisions knowing the rain data matches your exact schema. Because Pydantic AI is model-agnostic, you can use these tools with Anthropic, OpenAI, or local models. The validation layer ensures the weather data structure remains consistent regardless of the LLM you choose.

Wind and air quality monitoring

Your agent queries `get_wind_conditions`, `get_aqi_info`, and `get_visibility_data` to get verified metrics on wind speed, air pollution, and sight lines without risking hallucinated outputs. This prevents your system from acting on garbage data. The framework parses these metrics directly into strongly-typed Python objects. You can safely pass this data to downstream services knowing the types are guaranteed at runtime.

Setup guide

Set up Caiyun Weather / 彩云天气 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": {
        "caiyun-weather-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Caiyun Weather / 彩云天气 tools.",
)

result = await agent.run("List recent Caiyun Weather / 彩云天气 transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Caiyun Weather / 彩云天气. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Caiyun Weather / 彩云天气 MCP in Pydantic AI

The framework wraps the MCP Server tools and runs runtime checks on the returned JSON. If `get_aqi_info` returns an unexpected field type, Pydantic catches it immediately.

Install the slim package with MCP support, then instantiate `MCPToolset` with your Vinkius HTTP URL. Pass this toolset into your Agent's toolsets parameter to expose the tools.

Yes. Since the validation layer is model-agnostic, your local models can call `get_hourly_forecast` safely without breaking your schema.

No. Vinkius hosts and manages the MCP Server externally, so you only need to provide the streamable HTTP endpoint to your Python codebase.

Your latitude and longitude inputs are processed over encrypted transport layers directly to the weather API. The isolated sandbox ensures that your physical coordinates are never logged or exposed to third parties.

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