Open-Meteo Historical Weather MCP Server for OpenAI Agents SDK 3 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Open-Meteo Historical Weather through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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="Open-Meteo Historical Weather Assistant",
instructions=(
"You help users interact with Open-Meteo Historical Weather. "
"You have access to 3 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Open-Meteo Historical Weather"
)
print(result.final_output)
asyncio.run(main())
* 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 Open-Meteo Historical Weather MCP Server
Access 84 years of continuous weather records from 1940 to today for any location on Earth.
The OpenAI Agents SDK auto-discovers all 3 tools from Open-Meteo Historical Weather through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Open-Meteo Historical Weather, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
What you can do
- Historical Hourly — Temperature, humidity, precipitation, snowfall, weather codes, and wind for any past date range
- Historical Daily — Max/min temperatures, precipitation totals, sunshine duration, and dominant wind patterns
- Temperature Trends — Dedicated tool for long-term climate trend analysis with apparent temperature data
The Open-Meteo Historical Weather MCP Server exposes 3 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 Open-Meteo Historical Weather to OpenAI Agents SDK via MCP
Follow these steps to integrate the Open-Meteo Historical Weather 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 3 tools from Open-Meteo Historical Weather
Why Use OpenAI Agents SDK with the Open-Meteo Historical Weather MCP Server
OpenAI Agents SDK provides unique advantages when paired with Open-Meteo Historical Weather 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
Open-Meteo Historical Weather + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Open-Meteo Historical Weather MCP Server delivers measurable value.
Automated workflows: build agents that query Open-Meteo Historical Weather, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Open-Meteo Historical Weather, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Open-Meteo Historical Weather tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Open-Meteo Historical Weather to resolve tickets, look up records, and update statuses without human intervention
Open-Meteo Historical Weather MCP Tools for OpenAI Agents SDK (3)
These 3 tools become available when you connect Open-Meteo Historical Weather to OpenAI Agents SDK via MCP:
get_historical_daily
Get historical daily weather aggregates
get_historical_temperature
Includes hourly temperature, apparent temperature, and dewpoint. Get historical temperature trends for climate analysis
get_historical_weather
Provide latitude, longitude, start_date and end_date in YYYY-MM-DD format. Covers 84 years of global data. Get historical weather for any date range (1940–present)
Example Prompts for Open-Meteo Historical Weather in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Open-Meteo Historical Weather immediately.
"What was the weather in London on D-Day, June 6, 1944?"
"Compare average temperatures in São Paulo between 1950 and 2020"
"How much rain fell in Mumbai during the 2005 flood?"
Troubleshooting Open-Meteo Historical Weather MCP Server with OpenAI Agents SDK
Common issues when connecting Open-Meteo Historical Weather to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Open-Meteo Historical Weather + OpenAI Agents SDK FAQ
Common questions about integrating Open-Meteo Historical Weather MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Open-Meteo Historical Weather with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Open-Meteo Historical Weather to OpenAI Agents SDK
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.