How to Use the Weather (Open-Meteo) MCP in LlamaIndex

Q: How do I use Weather (Open-Meteo) with LlamaIndex for long-term planning?

You index the forecast data using weather.forecast. This allows you to build a knowledge base that spans months, not just days. You can query specific historical weather patterns easily.

Q: Does LlamaIndex help with multi-day forecasts from Weather (Open-Meteo)?

Absolutely. You call weather.forecast to get the 1–14 day data, and then index that structured output alongside your other documents. The system treats it like permanent knowledge.

Q: Can LlamaIndex handle comparing weather using Weather (Open-Meteo)?

Yes. Run weather.compare when you need a comparison, and then pass that result to the Indexer. You can then query your index months later about which of those cities is best for hiking.

Build RAG applications that index live data with LlamaIndex using the Weather (Open-Meteo) MCP Server.

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Connect Weather (Open-Meteo) MCP to LlamaIndex

Create your Vinkius account to connect Weather (Open-Meteo) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Weather (Open-Meteo) with LlamaIndex

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Search Historical Weather Data with LlamaIndex

LlamaIndex lets you take current weather data and turn it into searchable knowledge. Instead of just getting a list, the result from `weather.current` gets indexed into your vector store. Later, users can query past API results—like 'What was the AQI in Paris last month?'—and get an answer grounded in the actual recorded weather conditions.

Weather (Open-Meteo) and LlamaIndex: Compare Weather

Need to compare multiple cities for a trip? Use `weather.compare` to generate the comfort score, then index that entire comparison result. This way, your knowledge base isn't just text; it holds structured travel data. This is key for building robust RAG applications where live API data and user documents are combined into one queryable source.

Retrieve Best Time Windows with LlamaIndex

The `weather.best_time` tool finds the ideal 3-hour window for any outdoor activity over the next 72 hours, providing a score and tip. By indexing this output, you create specialized knowledge segments. Your application can then answer complex questions like, 'Based on last year's data, what was the best time to run in NYC?' using both historical documents and fresh `weather.best_time` results.

Setup guide

Set up Weather (Open-Meteo) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Weather (Open-Meteo) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Weather (Open-Meteo) tools.",
)
response = await agent.run("List recent Weather (Open-Meteo) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Open-Meteo. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Weather (Open-Meteo) MCP in LlamaIndex

You index the forecast data using `weather.forecast`. This allows you to build a knowledge base that spans months, not just days. You can query specific historical weather patterns easily.

Absolutely. You call `weather.forecast` to get the 1–14 day data, and then index that structured output alongside your other documents. The system treats it like permanent knowledge.

Yes. Run `weather.compare` when you need a comparison, and then pass that result to the Indexer. You can then query your index months later about which of those cities is best for hiking.

It supports various frameworks. The core strength here is that the tool output—whether it's current conditions or air quality data—always maintains a consistent structure for indexing.

It manages highly detailed temporal and geographical data, specifically covering weather metrics like temperature, precipitation, and air quality indices (PM2.5).

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