Open-Meteo Weather Forecast MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Open-Meteo Weather Forecast as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Open-Meteo Weather Forecast. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in Open-Meteo Weather Forecast?"
)
print(response)
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 Weather Forecast MCP Server
Connect your AI agent to the world's most comprehensive open weather API — no API key, no registration, no rate-limit headaches.
LlamaIndex agents combine Open-Meteo Weather Forecast tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- 16-Day Forecast — Hourly and daily predictions with temperature, precipitation probability, wind, humidity, UV index, and weather codes
- Current Conditions — Real-time snapshot including apparent temperature, cloud cover, wind gusts, and precipitation status
- Hourly Granularity — Dewpoint, visibility, snow depth, solar radiation, evapotranspiration, and CAPE convective index
- Daily Summaries — Max/min temperature, sunrise/sunset times, sunshine duration, and dominant wind direction
The Open-Meteo Weather Forecast MCP Server exposes 4 tools through the Vinkius. Connect it to LlamaIndex 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 Weather Forecast to LlamaIndex via MCP
Follow these steps to integrate the Open-Meteo Weather Forecast MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 4 tools from Open-Meteo Weather Forecast
Why Use LlamaIndex with the Open-Meteo Weather Forecast MCP Server
LlamaIndex provides unique advantages when paired with Open-Meteo Weather Forecast through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Open-Meteo Weather Forecast tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Open-Meteo Weather Forecast tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Open-Meteo Weather Forecast, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Open-Meteo Weather Forecast tools were called, what data was returned, and how it influenced the final answer
Open-Meteo Weather Forecast + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Open-Meteo Weather Forecast MCP Server delivers measurable value.
Hybrid search: combine Open-Meteo Weather Forecast real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Open-Meteo Weather Forecast to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Open-Meteo Weather Forecast for fresh data
Analytical workflows: chain Open-Meteo Weather Forecast queries with LlamaIndex's data connectors to build multi-source analytical reports
Open-Meteo Weather Forecast MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect Open-Meteo Weather Forecast to LlamaIndex via MCP:
get_current_weather
Provide latitude and longitude. Get current weather conditions for any location
get_daily_summary
Get daily weather summary with sunrise/sunset
get_hourly_details
Get detailed hourly weather data
get_weather_forecast
Provide latitude and longitude coordinates. Get weather forecast for any location (up to 16 days)
Example Prompts for Open-Meteo Weather Forecast in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Open-Meteo Weather Forecast immediately.
"What's the weather forecast for Tokyo this week?"
"What are the current weather conditions in New York?"
"Will it snow in Munich in the next 10 days?"
Troubleshooting Open-Meteo Weather Forecast MCP Server with LlamaIndex
Common issues when connecting Open-Meteo Weather Forecast to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpen-Meteo Weather Forecast + LlamaIndex FAQ
Common questions about integrating Open-Meteo Weather Forecast MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Open-Meteo Weather Forecast 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 Open-Meteo Weather Forecast to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.