Open-Meteo Historical Weather MCP Server for LlamaIndex 3 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Open-Meteo Historical Weather 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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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 Historical Weather. "
"You have 3 tools available."
),
)
response = await agent.run(
"What tools are available in Open-Meteo Historical Weather?"
)
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 Historical Weather MCP Server
Access 84 years of continuous weather records from 1940 to today for any location on Earth.
LlamaIndex agents combine Open-Meteo Historical Weather tool responses with indexed documents for comprehensive, grounded answers. Connect 3 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
- 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 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 Historical Weather to LlamaIndex via MCP
Follow these steps to integrate the Open-Meteo Historical Weather 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 3 tools from Open-Meteo Historical Weather
Why Use LlamaIndex with the Open-Meteo Historical Weather MCP Server
LlamaIndex provides unique advantages when paired with Open-Meteo Historical Weather through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Open-Meteo Historical Weather tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Open-Meteo Historical Weather tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Open-Meteo Historical Weather, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Open-Meteo Historical Weather tools were called, what data was returned, and how it influenced the final answer
Open-Meteo Historical Weather + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Open-Meteo Historical Weather MCP Server delivers measurable value.
Hybrid search: combine Open-Meteo Historical Weather real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Open-Meteo Historical Weather 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 Historical Weather for fresh data
Analytical workflows: chain Open-Meteo Historical Weather queries with LlamaIndex's data connectors to build multi-source analytical reports
Open-Meteo Historical Weather MCP Tools for LlamaIndex (3)
These 3 tools become available when you connect Open-Meteo Historical Weather to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Open-Meteo Historical Weather to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpen-Meteo Historical Weather + LlamaIndex FAQ
Common questions about integrating Open-Meteo Historical Weather 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 Historical Weather with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Microsoft's framework for multi-agent collaborative conversations.
Connect Open-Meteo Historical Weather to LlamaIndex
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.