MeteoSource 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 MeteoSource 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 MeteoSource. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in MeteoSource?"
)
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 MeteoSource MCP Server
Empower your AI agent to orchestrate your entire meteorological research and weather auditing workflow with MeteoSource, the comprehensive source for hyper-local weather data. By connecting the MeteoSource API to your agent, you transform complex forecast searches into a natural conversation. Your agent can instantly search for monitored places, audit daily and hourly forecasts, and retrieve timezone metadata without you ever touching a weather portal. Whether you are planning outdoor events or conducting regional climate audits, your agent acts as a real-time meteorological consultant, ensuring your data is always precise and localized.
LlamaIndex agents combine MeteoSource 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
- Place Auditing — Search for thousands of global locations and retrieve high-resolution place IDs and geographic metadata.
- Forecast Oversight — Audit comprehensive point forecasts, including current conditions, daily summaries, and hourly breakdowns.
- Geographic Discovery — Find the nearest monitored place by latitude and longitude to maintain strict organizational control over local data.
- Temporal Intelligence — Query timezone information for specific places to assist in time-sensitive logistics and event planning.
- Operational Monitoring — Check API status to ensure your meteorological research workflow is always operational.
The MeteoSource 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 MeteoSource to LlamaIndex via MCP
Follow these steps to integrate the MeteoSource 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 MeteoSource
Why Use LlamaIndex with the MeteoSource MCP Server
LlamaIndex provides unique advantages when paired with MeteoSource through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine MeteoSource tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain MeteoSource tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query MeteoSource, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what MeteoSource tools were called, what data was returned, and how it influenced the final answer
MeteoSource + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the MeteoSource MCP Server delivers measurable value.
Hybrid search: combine MeteoSource real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query MeteoSource 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 MeteoSource for fresh data
Analytical workflows: chain MeteoSource queries with LlamaIndex's data connectors to build multi-source analytical reports
MeteoSource MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect MeteoSource to LlamaIndex via MCP:
check_api_status
Check if the MeteoSource service is operational
get_nearest_weather_place
Find the nearest monitored place by latitude and longitude
get_place_timezone
Get timezone information for a specific place_id
get_point_forecast
Get weather forecast for a specific place_id
search_weather_places
Search for a place by name to get its place_id for forecasts
Example Prompts for MeteoSource in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with MeteoSource immediately.
"Get weather forecast for 'London' using MeteoSource."
"Search for weather station near latitude 48.8566 and longitude 2.3522."
"What is the timezone for place 'tokyo'?"
Troubleshooting MeteoSource MCP Server with LlamaIndex
Common issues when connecting MeteoSource to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMeteoSource + LlamaIndex FAQ
Common questions about integrating MeteoSource 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 MeteoSource 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 MeteoSource to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.