How to Use the Meteostat MCP in OpenAI Agents SDK

Q: How do I connect Meteostat to my OpenAI Agents SDK project?

You define the server using MCPServerStreamableHttp pointing to your Vinkius endpoint. Pass this object into your Agent constructor to give your system automatic access to all 10 tools.

Q: Can I cache Meteostat responses in OpenAI Agents SDK?

You should set cacheToolsList=True in your server configuration. This reduces overhead by storing the tool definitions locally, which keeps your agent's startup time fast.

Get historical weather data directly into your OpenAI Agents SDK production pipelines.

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Connect Meteostat MCP to OpenAI Agents SDK

Create your Vinkius account to connect Meteostat to OpenAI Agents SDK 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 Meteostat with OpenAI Agents SDK

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Historical point data for agents

Feed your agents precise weather metrics using `point_daily`, `point_hourly`, and `point_monthly`. These tools allow your system to pull historical records for any coordinate set without complex parsing. Your agent handles data retrieval as an async operation. By using `point_normals`, you provide your models with climate baselines to ground their analysis in real-world conditions.

Station-specific telemetry for OpenAI Agents SDK

Access raw station data with `stations_daily` and `stations_hourly` to build local weather models. The server manages the connection, letting your agent focus on processing the returned observation sets. Use `stations_meta` to verify station identifiers before you run queries. This ensures your agent isn't wasting tokens on invalid station IDs or incorrect geographic data.

Geo-located weather discovery

Locate relevant sensors near your project area using `stations_nearby`. Your agent can dynamically find the closest reporting station based on user input coordinates. Once the station is found, call `stations_normals` to get long-term climate trends. This MCP server handles the lookup logic so your agent stays focused on the primary task.

Setup guide

Set up Meteostat MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Meteostat tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Meteostat tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Meteostat tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Meteostat Agent",
            instructions="You have access to Meteostat tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Meteostat MCP in OpenAI Agents SDK

You define the server using `MCPServerStreamableHttp` pointing to your Vinkius endpoint. Pass this object into your `Agent` constructor to give your system automatic access to all 10 tools.

Yes. Since the tools return structured data, the agent's built-in validation layer inspects the output before it reaches your downstream logic. This prevents malformed weather data from entering your production flow.

You should set `cacheToolsList=True` in your server configuration. This reduces overhead by storing the tool definitions locally, which keeps your agent's startup time fast.

The server returns a standard error message. Your agent handles this through your existing try-catch blocks to ensure your pipeline doesn't hang.

Vinkius manages the authentication layer entirely. You only need to provide your endpoint token; the actual provider keys are handled securely within the isolated sandbox environment.

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