How to Use the NOAA Climate — Historical Weather Records MCP in OpenAI Agents SDK

Q: How do I connect NOAA Climate — Historical Weather Records to OpenAI Agents SDK?

Install the openai-agents package. Create an MCPServerStreamableHttp instance with your endpoint URL, and pass it to the mcpservers list in your Agent constructor. Set cacheToolsList=True to speed up tool discovery.

Q: Can OpenAI Agents SDK query daily temperature records using this MCP Server?

Yes. The agent uses the getdailydata tool to pull max temperatures, min temperatures, and precipitation from the GHCN-Daily archive. It filters by date range and specific station IDs.

Q: Does NOAA Climate — Historical Weather Records work with OpenAI Agents SDK handoffs?

It works perfectly. You can have one agent run searchstations to map an area, then hand off those station IDs to an analysis agent that runs getmonthlysummary for long-term trends.

Connect NOAA's massive weather archive directly to your OpenAI Agents SDK production system.

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Connect NOAA Climate — Historical Weather Records MCP to OpenAI Agents SDK

Create your Vinkius account to connect NOAA Climate — Historical Weather Records 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 NOAA Climate — Historical Weather Records with OpenAI Agents SDK

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Historical weather data for OpenAI Agents

Your agent needs baseline data to evaluate climate risk. The `get_climate_normals` tool pulls the 30-year statistical baseline for any location. This gives your OpenAI Agents SDK setup the exact metrics required to define normal weather patterns before assessing anomalies. You do not want agents guessing about historical extremes. By calling `get_yearly_summary`, the system retrieves hard annual averages and extreme temperature spikes directly from NOAA's GSOY dataset. Guardrails validate the agent's action before it processes the decades-long climate timeline.

Pinpoint specific weather stations

Before you pull daily records, your agent has to find the right physical hardware. The `search_stations` tool takes a bounding box and returns the exact NCEI station IDs, names, and coordinates. Once your agent identifies a target station, it hands off the ID to a specialized data-gathering agent. That secondary agent then runs `get_daily_data` to extract exact temperatures, precipitation totals, and snow depth from the GHCN-Daily archive.

Analyze monthly climate trends

Daily data is too granular for macro-level trend analysis. You need the `get_monthly_summary` tool to pull temperature averages and heating degree days aggregated at the monthly level. This MCP Server feeds that aggregated data straight into your tracing pipeline. You can watch the entire OpenAI dashboard as your agent compiles GSOM records into a coherent climate risk assessment.

Setup guide

Set up NOAA Climate — Historical Weather Records 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 NOAA Climate — Historical Weather Records tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate NOAA Climate — Historical Weather Records 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="NOAA Climate — Historical Weather Records Agent",
            instructions="You have access to NOAA Climate — Historical Weather Records 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 NOAA Climate — Historical Weather Records MCP in OpenAI Agents SDK

Install the `openai-agents` package. Create an `MCPServerStreamableHttp` instance with your endpoint URL, and pass it to the `mcp_servers` list in your Agent constructor. Set `cacheToolsList=True` to speed up tool discovery.

Yes. The agent uses the `get_daily_data` tool to pull max temperatures, min temperatures, and precipitation from the GHCN-Daily archive. It filters by date range and specific station IDs.

It works perfectly. You can have one agent run `search_stations` to map an area, then hand off those station IDs to an analysis agent that runs `get_monthly_summary` for long-term trends.

The archive covers over a century of records depending on the station. You can pull deep historical context using `get_yearly_summary` or establish a 30-year baseline with `get_climate_normals`.

This server exclusively handles public domain meteorological metrics like TMAX, TMIN, and PRCP from NOAA. Your agent reads historical temperature and precipitation numbers. No user data flows into the NCEI API, keeping your internal risk models entirely isolated on your end.

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