How to Use the Caiyun Weather / 彩云天气 MCP in LlamaIndex

Q: Does LlamaIndex support filtering Caiyun Weather / 彩云天气 tools?

You restrict access using the allowedtools parameter. This prevents your RAG app from calling getvisibilitydata if it only needs temperature readings.

Index minute-level Chinese weather data directly into your LlamaIndex RAG applications.

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Connect Caiyun Weather / 彩云天气 MCP to LlamaIndex

Create your Vinkius account to connect Caiyun Weather / 彩云天气 to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Caiyun Weather / 彩云天气 with LlamaIndex

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Index LlamaIndex MCP Server Data

`get_daily_forecast` pulls structured meteorological data that LlamaIndex instantly embeds into your vector store. Your RAG setup now queries against actual API responses instead of static documents. Users ask questions about historical or upcoming conditions and get answers grounded in reality. The index cross-references local documents with `get_hourly_forecast` to schedule outdoor events perfectly.

Ground Responses in Air Quality

`get_aqi_info` supplies precise pollution metrics for your knowledge base. LlamaIndex treats this live environmental data as context for user queries regarding health or travel. A query about safe running routes checks `get_precipitation_probability` alongside the AQI stats. The system synthesizes these live variables with embedded maps to generate accurate, safe recommendations.

Embed Minute-Level Rainfall

`get_minutely_rain` feeds exact precipitation timelines into your LlamaIndex data structures. Your application knows exactly when the rain stops at a specific GPS coordinate. Combining this with `get_wind_conditions` creates a powerful situational awareness engine. Semantic search retrieves these live metrics instantly when a user asks about current outdoor conditions.

Setup guide

Set up Caiyun Weather / 彩云天气 MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Caiyun Weather / 彩云天气 MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Caiyun Weather / 彩云天气 tools.",
)
response = await agent.run("List recent Caiyun Weather / 彩云天气 data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Caiyun Weather / 彩云天气. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Caiyun Weather / 彩云天气 MCP in LlamaIndex

Install llama-index-tools-mcp via pip. Create a BasicMCPClient with the Vinkius URL, wrap it in McpToolSpec, and pass the async tool list to your FunctionAgent.

You configure caching at the vector store level. Fetching get_realtime_weather updates the index, and subsequent identical queries can hit the cache to save API calls.

You restrict access using the allowed_tools parameter. This prevents your RAG app from calling get_visibility_data if it only needs temperature readings.

LlamaIndex chunks the JSON output from the MCP endpoint automatically. It embeds the fragmented weather data so the LLM can retrieve specific timeframes efficiently.

Vinkius processes your location markers inside a zero-trust, ephemeral sandbox. The server drops all memory of your geographic queries the second the weather data transmits back to your client.

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