How far back does the data go?

GHCN-Daily records go back to the 1700s for some stations, with widespread coverage since the 1890s. Over 100,000 stations worldwide, with the densest network in the United States.

What is the difference between GHCN-D, GSOM, and GSOY?

GHCN-Daily provides day-by-day records. GSOM (Global Summary of the Month) aggregates these into monthly averages and totals. GSOY (Global Summary of the Year) provides annual summaries.

Are observations available for international locations?

Yes, while NOAA is a US agency, the GHCN incorporates data from over 100,000 stations worldwide, though the highest density remains in North America, Europe, and Australia.

NOAA Climate — Historical Weather Records MCP Server for OpenAI Agents SDK 5 tools — connect in under 2 minutes

Name: NOAA Climate — Historical Weather Records
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 5 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect NOAA Climate — Historical Weather Records through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

Get MCP Server for AI Agents

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Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="NOAA Climate — Historical Weather Records Assistant",
            instructions=(
                "You help users interact with NOAA Climate — Historical Weather Records. "
                "You have access to 5 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from NOAA Climate — Historical Weather Records"
        )
        print(result.final_output)

asyncio.run(main())

NOAA Climate — Historical Weather Records

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About NOAA Climate — Historical Weather Records MCP Server

The planet's largest archive of daily weather records, freely accessible.

The OpenAI Agents SDK auto-discovers all 5 tools from NOAA Climate — Historical Weather Records through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries NOAA Climate — Historical Weather Records, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Daily Data (GHCN-D) — Temperature, precipitation, snow, wind for 100K+ stations
Monthly Summaries (GSOM) — Monthly aggregates
Annual Summaries (GSOY) — Yearly climate data
Climate Normals — 30-year baseline (1991-2020)
Station Search — Find stations by location or name

Global Coverage

GHCN-Daily has worldwide stations, with densest coverage in the US, Europe, and Australia.

The NOAA Climate — Historical Weather Records MCP Server exposes 5 tools through the Vinkius. Connect it to OpenAI Agents SDK 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 NOAA Climate — Historical Weather Records to OpenAI Agents SDK via MCP

Follow these steps to integrate the NOAA Climate — Historical Weather Records MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 5 tools from NOAA Climate — Historical Weather Records

Why Use OpenAI Agents SDK with the NOAA Climate — Historical Weather Records MCP Server

OpenAI Agents SDK provides unique advantages when paired with NOAA Climate — Historical Weather Records through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

NOAA Climate — Historical Weather Records + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the NOAA Climate — Historical Weather Records MCP Server delivers measurable value.

Automated workflows: build agents that query NOAA Climate — Historical Weather Records, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries NOAA Climate — Historical Weather Records, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through NOAA Climate — Historical Weather Records tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query NOAA Climate — Historical Weather Records to resolve tickets, look up records, and update statuses without human intervention

NOAA Climate — Historical Weather Records MCP Tools for OpenAI Agents SDK (5)

These 5 tools become available when you connect NOAA Climate — Historical Weather Records to OpenAI Agents SDK via MCP:

get_climate_normals

This is the statistical baseline that defines "normal" weather for any location. Get 30-year climate normals — the baseline for what is "normal" weather

get_daily_data

This is the planet's largest archive of daily weather records. Filter by station, data types (TMAX, TMIN, PRCP, SNOW, SNWD), and date range. Stations are worldwide but densest coverage is in the US. Get daily weather data (GHCN-Daily): temperatures, precipitation, snow

get_monthly_summary

Monthly aggregates of temperature averages, precipitation totals, and degree days. Less granular than daily but ideal for climate trend analysis. Get monthly climate summary (GSOM): average temp, total precipitation, heating degree days

get_yearly_summary

Yearly temperature averages, precipitation totals, and extreme values. Perfect for long-term climate analysis spanning decades. Get annual climate summary (GSOY): yearly averages and extremes

search_stations

Returns station IDs, names, and locations for use with other climate tools. Search NCEI weather stations by location bounding box or keyword

Example Prompts for NOAA Climate — Historical Weather Records in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with NOAA Climate — Historical Weather Records immediately.

"Get daily temperatures for Central Park, NYC in January 2024"

"Show me the total monthly precipitation for Seattle in 2023."

"What are the 30-year climate normals for Miami?"

Troubleshooting NOAA Climate — Historical Weather Records MCP Server with OpenAI Agents SDK

Common issues when connecting NOAA Climate — Historical Weather Records to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

NOAA Climate — Historical Weather Records + OpenAI Agents SDK FAQ

Common questions about integrating NOAA Climate — Historical Weather Records MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.