Open-Meteo Full Access MCP Server for LlamaIndex 15 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Open-Meteo Full Access 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 Full Access. "
"You have 15 tools available."
),
)
response = await agent.run(
"What tools are available in Open-Meteo Full Access?"
)
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 Full Access MCP Server
The definitive Mega-Server for weather and climate intelligence. Why install 7 servers when one does it all?
LlamaIndex agents combine Open-Meteo Full Access tool responses with indexed documents for comprehensive, grounded answers. Connect 15 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
- Live Weather — 16-day forecast + current conditions for any GPS coordinate
- 84 Years of History — Hourly weather archives from 1940 to today
- Ocean Intelligence — Wave height, swell, currents, sea surface temperature at 5km
- Air Safety — PM2.5, PM10, O₃, pollen counts, European & US AQI
- Climate Future — IPCC projections to 2100 + ensemble probabilistic forecasts
- Flood Risk — GloFAS river discharge with 40 years of reanalysis + 7 months forward
- Location Tools — Global geocoding and 90m terrain elevation
The Open-Meteo Full Access MCP Server exposes 15 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 Full Access to LlamaIndex via MCP
Follow these steps to integrate the Open-Meteo Full Access 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 15 tools from Open-Meteo Full Access
Why Use LlamaIndex with the Open-Meteo Full Access MCP Server
LlamaIndex provides unique advantages when paired with Open-Meteo Full Access through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Open-Meteo Full Access tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Open-Meteo Full Access tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Open-Meteo Full Access, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Open-Meteo Full Access tools were called, what data was returned, and how it influenced the final answer
Open-Meteo Full Access + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Open-Meteo Full Access MCP Server delivers measurable value.
Hybrid search: combine Open-Meteo Full Access real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Open-Meteo Full Access 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 Full Access for fresh data
Analytical workflows: chain Open-Meteo Full Access queries with LlamaIndex's data connectors to build multi-source analytical reports
Open-Meteo Full Access MCP Tools for LlamaIndex (15)
These 15 tools become available when you connect Open-Meteo Full Access to LlamaIndex via MCP:
get_air_quality
5, PM10, ozone, NO2, SO2, CO concentrations. Get air quality pollutant concentrations
get_aqi_index
Get AQI (European and US standards)
get_climate_projection
Get IPCC climate projections (2015–2100)
get_current_weather
Get current weather conditions
get_elevation
Get terrain elevation for any coordinates
get_ensemble_forecast
Get probabilistic multi-model ensemble forecast
get_flood_forecast
Get flood forecast up to 7 months ahead
get_historical_daily
Get historical daily aggregates
get_historical_weather
Covers 84 years. Get historical weather (1940–present)
get_marine_forecast
Get marine wave forecast at 5km resolution
get_ocean_currents
Get ocean currents and sea surface temperature
get_pollen_forecast
Get pollen and allergen forecast
get_river_discharge
Get river discharge data at 5km resolution
get_weather_forecast
Get weather forecast for any location (up to 16 days)
search_location
Search cities and locations globally
Example Prompts for Open-Meteo Full Access in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Open-Meteo Full Access immediately.
"Full weather briefing for a yacht trip from Lisbon to Madeira next week"
"Climate risk assessment for a new data center in Singapore"
"What was the weather like on the day I was born? July 15, 1990 in Rome"
Troubleshooting Open-Meteo Full Access MCP Server with LlamaIndex
Common issues when connecting Open-Meteo Full Access to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpen-Meteo Full Access + LlamaIndex FAQ
Common questions about integrating Open-Meteo Full Access 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 Full Access 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 Full Access to LlamaIndex
Get your token, paste the configuration, and start using 15 tools in under 2 minutes. No API key management needed.