QWeather / 和风天气 MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add QWeather / 和风天气 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 QWeather / 和风天气. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in QWeather / 和风天气?"
)
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 QWeather / 和风天气 MCP Server
Empower your AI agent to orchestrate your daily planning and environmental monitoring with QWeather (和风天气), the premier commercial weather platform in China. By connecting QWeather to your agent, you transform complex meteorological data and location-based environmental searches into a natural conversation. Your agent can instantly retrieve real-time weather, 15-day forecasts, air quality indices, severe weather warnings, and astronomical data without you ever needing to navigate a technical dashboard. Whether you are planning outdoor operations or auditing air quality across different regions, your agent acts as a real-time environmental consultant, providing accurate and fast results from a single, unified source.
LlamaIndex agents combine QWeather / 和风天气 tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Weather Orchestration — Retrieve current weather and detailed forecasts (up to 15 days) for any location worldwide.
- Air Quality Auditing — Monitor real-time AQI, PM2.5, and PM10 levels to ensure safe operating conditions.
- Life Index Insights — Access specialized indices for UV radiation, clothing recommendations, and car washing suitability.
- Warning Monitoring — Audit active severe weather warnings to maintain safety and organizational continuity.
- Geographic Discovery — Search for location IDs and coordinates using keywords to refine your regional tracking.
The QWeather / 和风天气 MCP Server exposes 10 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 QWeather / 和风天气 to LlamaIndex via MCP
Follow these steps to integrate the QWeather / 和风天气 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 10 tools from QWeather / 和风天气
Why Use LlamaIndex with the QWeather / 和风天气 MCP Server
LlamaIndex provides unique advantages when paired with QWeather / 和风天气 through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine QWeather / 和风天气 tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain QWeather / 和风天气 tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query QWeather / 和风天气, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what QWeather / 和风天气 tools were called, what data was returned, and how it influenced the final answer
QWeather / 和风天气 + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the QWeather / 和风天气 MCP Server delivers measurable value.
Hybrid search: combine QWeather / 和风天气 real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query QWeather / 和风天气 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 QWeather / 和风天气 for fresh data
Analytical workflows: chain QWeather / 和风天气 queries with LlamaIndex's data connectors to build multi-source analytical reports
QWeather / 和风天气 MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect QWeather / 和风天气 to LlamaIndex via MCP:
get_air_now
5, etc.). Get current air quality
get_indices
Get daily life indices
get_moon_astronomy
Get moonrise and moonset times
get_sun_astronomy
Get sunrise and sunset times
get_warning
Get weather warnings
get_weather_24h
Get 24-hour weather forecast
get_weather_3d
Get 3-day weather forecast
get_weather_7d
Get 7-day weather forecast
get_weather_now
Get current weather
lookup_location
Search for location ID
Example Prompts for QWeather / 和风天气 in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with QWeather / 和风天气 immediately.
"What is the current weather in Beijing (101010100)?"
"Check the air quality for Shanghai today."
"Find the location ID for 'Hangzhou'."
Troubleshooting QWeather / 和风天气 MCP Server with LlamaIndex
Common issues when connecting QWeather / 和风天气 to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpQWeather / 和风天气 + LlamaIndex FAQ
Common questions about integrating QWeather / 和风天气 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 QWeather / 和风天气 with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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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 QWeather / 和风天气 to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.