Open-Meteo MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Open-Meteo as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in 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 Open-Meteo MCP Server
Connect to Open-Meteo and access global weather forecasts through natural conversation — no API key needed.
LlamaIndex agents combine Open-Meteo tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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 Weather — Get real-time temperature, humidity, wind, precipitation and conditions
- 7-Day Forecast — Hourly and daily forecasts up to 16 days ahead with 50+ weather variables
- Historical Weather — Access archived weather data going back to 1940 for any location
- Air Quality — Get PM2.5, PM10, NO2, O3, SO2, CO and UV index forecasts
- Geocoding — Find coordinates for any city or place name
- Elevation — Get elevation data for any coordinates
The Open-Meteo MCP Server exposes 5 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 to LlamaIndex via MCP
Follow these steps to integrate the 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 5 tools from Open-Meteo
Why Use LlamaIndex with the Open-Meteo MCP Server
LlamaIndex provides unique advantages when paired with Open-Meteo through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Open-Meteo tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Open-Meteo tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Open-Meteo, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Open-Meteo tools were called, what data was returned, and how it influenced the final answer
Open-Meteo + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Open-Meteo MCP Server delivers measurable value.
Hybrid search: combine Open-Meteo real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query 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 Open-Meteo for fresh data
Analytical workflows: chain Open-Meteo queries with LlamaIndex's data connectors to build multi-source analytical reports
Open-Meteo MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect Open-Meteo to LlamaIndex via MCP:
get_air_quality
5, PM10, nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide, dust, pollen and UV index. Requires latitude and longitude. Returns hourly data for up to 7 days. Common variables: pm2_5, pm10, nitrogen_dioxide, ozone, sulphur_dioxide, carbon_monoxide, dust, uv_index, alder_pollen, grass_pollen. Get air quality forecast for a location
get_elevation
Useful for hiking, aviation and geographic research. Get elevation for coordinates
get_forecast
Requires latitude and longitude. Supports hourly, daily and current weather variables. Common variables: temperature_2m, relative_humidity_2m, precipitation, rain, snowfall, wind_speed_10m, wind_direction_10m, wind_gusts_10m, weather_code, cloud_cover, pressure_msl, uv_index, visibility, apparent_temperature, dew_point_2m, sunshine_duration. Set past_days to include historical data (0-92 days). Set forecast_days for forecast length (0-16 days, default 7). Timezone defaults to GMT; use "auto" for local timezone. Get weather forecast for a location
get_geocoding
Useful for finding coordinates to use with weather tools. Returns up to 10 results by default. Find coordinates for a place name
get_historical_weather
Requires latitude, longitude, start date and end date (YYYY-MM-DD format). Supports the same hourly variables as the forecast API. Historical data goes back to 1940 for most locations. Use get_geocoding to find coordinates for a city name. Get historical weather data for a location
Example Prompts for Open-Meteo in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Open-Meteo immediately.
"What's the weather forecast for São Paulo this week?"
"What was the temperature in Tokyo on July 15, 2024?"
"What's the air quality in Beijing right now?"
Troubleshooting Open-Meteo MCP Server with LlamaIndex
Common issues when connecting Open-Meteo to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpen-Meteo + LlamaIndex FAQ
Common questions about integrating 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 Open-Meteo 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 to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.