Weather (Open-Meteo) MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Weather (Open-Meteo) as an MCP tool provider through 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 Weather (Open-Meteo). "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Weather (Open-Meteo)?"
)
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 Weather (Open-Meteo) MCP Server
Connect to Open-Meteo and empower your AI agent with high-precision meteorological data through natural conversation. This server provides comprehensive weather insights for any location on Earth without requiring complex API configurations.
LlamaIndex agents combine Weather (Open-Meteo) tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through 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
- Current Conditions — Retrieve real-time temperature, humidity, wind speed, and precipitation for any city
- Precise Forecasts — Get 7-day daily forecasts or detailed 24-hour hourly breakdowns to plan your activities
- Air Quality — Monitor US AQI levels and dominant pollutants (PM2.5, PM10, Ozone) with health recommendations
- Weather Alerts — Receive critical notifications for severe weather conditions like storms, heavy snow, or high UV levels
- City Comparisons — Compare weather conditions between two different cities to help with travel or logistics planning
- Best Time Analysis — Find the optimal window for outdoor activities based on temperature and precipitation thresholds
- Global Coverage — Seamlessly geocode any city name into coordinates for instant weather retrieval
The Weather (Open-Meteo) MCP Server exposes 7 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 Weather (Open-Meteo) to LlamaIndex via MCP
Follow these steps to integrate the Weather (Open-Meteo) 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 7 tools from Weather (Open-Meteo)
Why Use LlamaIndex with the Weather (Open-Meteo) MCP Server
LlamaIndex provides unique advantages when paired with Weather (Open-Meteo) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Weather (Open-Meteo) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Weather (Open-Meteo) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Weather (Open-Meteo), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Weather (Open-Meteo) tools were called, what data was returned, and how it influenced the final answer
Weather (Open-Meteo) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Weather (Open-Meteo) MCP Server delivers measurable value.
Hybrid search: combine Weather (Open-Meteo) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Weather (Open-Meteo) 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 Weather (Open-Meteo) for fresh data
Analytical workflows: chain Weather (Open-Meteo) queries with LlamaIndex's data connectors to build multi-source analytical reports
Weather (Open-Meteo) MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Weather (Open-Meteo) to LlamaIndex via MCP:
weather.air_quality
Always display the health_recommendation from the result. AQI scale: 0-50=Good, 51-100=Moderate, 101-150=Unhealthy for Sensitive Groups, 151-200=Unhealthy, 201-300=Very Unhealthy, 301+=Hazardous. Get current air quality index (AQI), PM2.5, PM10, ozone, and NO2 for any city
weather.alerts
Alerts are computed from current conditions and the 7-day forecast. Returns an empty list if no significant conditions are detected. Get active weather alerts and advisories for any city, derived from forecast data
weather.best_time
This tool analyses hourly data for 72 hours and scores each 3-hour window using activity-specific criteria. Supports activities: general, hiking, beach, cycling, running, outdoor_work, photography, picnic. Always show the tip from the top-ranked window. Find the best time windows in the next 72 hours to do an outdoor activity in any city, with a comfort score
weather.compare
g. "which city has better weather?", "should I go to Lisbon or Madrid this weekend?", "compare weather in NYC, London, and Tokyo"). Accepts 2 to 5 cities. Computes a comfort score for each and highlights the best option. Compare current weather conditions across multiple cities side by side, with comfort scores
weather.current
Accept natural language city names (e.g. "São Paulo", "New York", "Paris, France"). Do NOT use for forecasts — use weather.forecast for that. Get current weather conditions for any city in the world
weather.forecast
Default to 7 days if the user does not specify. Max 14 days. For today's hour-by-hour breakdown, use weather.hourly instead. Get a multi-day weather forecast (1–14 days) for any city in the world
weather.hourly
Always covers the next 24 hours from now. For multi-day overview, use weather.forecast instead. Get an hour-by-hour weather forecast for the next 24 hours for any city
Example Prompts for Weather (Open-Meteo) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Weather (Open-Meteo) immediately.
"What's the weather like in Tokyo right now?"
"Give me the 7-day forecast for London."
"Compare the weather between New York and Miami."
Troubleshooting Weather (Open-Meteo) MCP Server with LlamaIndex
Common issues when connecting Weather (Open-Meteo) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpWeather (Open-Meteo) + LlamaIndex FAQ
Common questions about integrating Weather (Open-Meteo) 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 Weather (Open-Meteo) with your favorite client
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Microsoft's framework for multi-agent collaborative conversations.
Connect Weather (Open-Meteo) to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.