MeteoSource MCP Server for OpenAI Agents SDK 5 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect MeteoSource 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="MeteoSource Assistant",
instructions=(
"You help users interact with MeteoSource. "
"You have access to 5 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from MeteoSource"
)
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 MeteoSource MCP Server
Empower your AI agent to orchestrate your entire meteorological research and weather auditing workflow with MeteoSource, the comprehensive source for hyper-local weather data. By connecting the MeteoSource API to your agent, you transform complex forecast searches into a natural conversation. Your agent can instantly search for monitored places, audit daily and hourly forecasts, and retrieve timezone metadata without you ever touching a weather portal. Whether you are planning outdoor events or conducting regional climate audits, your agent acts as a real-time meteorological consultant, ensuring your data is always precise and localized.
The OpenAI Agents SDK auto-discovers all 5 tools from MeteoSource through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries MeteoSource, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
What you can do
- Place Auditing — Search for thousands of global locations and retrieve high-resolution place IDs and geographic metadata.
- Forecast Oversight — Audit comprehensive point forecasts, including current conditions, daily summaries, and hourly breakdowns.
- Geographic Discovery — Find the nearest monitored place by latitude and longitude to maintain strict organizational control over local data.
- Temporal Intelligence — Query timezone information for specific places to assist in time-sensitive logistics and event planning.
- Operational Monitoring — Check API status to ensure your meteorological research workflow is always operational.
The MeteoSource 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 MeteoSource to OpenAI Agents SDK via MCP
Follow these steps to integrate the MeteoSource 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 MeteoSource
Why Use OpenAI Agents SDK with the MeteoSource MCP Server
OpenAI Agents SDK provides unique advantages when paired with MeteoSource 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
MeteoSource + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the MeteoSource MCP Server delivers measurable value.
Automated workflows: build agents that query MeteoSource, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries MeteoSource, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through MeteoSource tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query MeteoSource to resolve tickets, look up records, and update statuses without human intervention
MeteoSource MCP Tools for OpenAI Agents SDK (5)
These 5 tools become available when you connect MeteoSource to OpenAI Agents SDK via MCP:
check_api_status
Check if the MeteoSource service is operational
get_nearest_weather_place
Find the nearest monitored place by latitude and longitude
get_place_timezone
Get timezone information for a specific place_id
get_point_forecast
Get weather forecast for a specific place_id
search_weather_places
Search for a place by name to get its place_id for forecasts
Example Prompts for MeteoSource in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with MeteoSource immediately.
"Get weather forecast for 'London' using MeteoSource."
"Search for weather station near latitude 48.8566 and longitude 2.3522."
"What is the timezone for place 'tokyo'?"
Troubleshooting MeteoSource MCP Server with OpenAI Agents SDK
Common issues when connecting MeteoSource to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
MeteoSource + OpenAI Agents SDK FAQ
Common questions about integrating MeteoSource 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 MeteoSource with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
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 MeteoSource to OpenAI Agents SDK
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.