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

Q: How do I configure Caiyun Weather / 彩云天气 inside AutoGen?

Install autogen-extmcp first. Use mcpservertools with StreamableHttpServerParams pointing to Vinkius, then pass the resulting list to your AssistantAgent.

Give your AutoGen debating agents access to China's most precise weather forecasting system.

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Connect Caiyun Weather / 彩云天气 MCP to AutoGen

Create your Vinkius account to connect Caiyun Weather / 彩云天气 to AutoGen 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 AutoGen

Ask AI about this MCP

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Perplexity

Ground AutoGen MCP Server Chats

`get_realtime_weather` injects hard facts into your multi-agent conversations. A logistics agent and a safety agent can argue over a delivery route using identical, live atmospheric data. The consensus shifts when the system introduces actual constraints. One agent pulls `get_visibility_data` to prove a highway is unsafe, forcing the routing agent to propose an alternative path.

Multi-Agent Storm Tracking

`get_minutely_rain` acts as the definitive source of truth during agent negotiations. A scheduling agent uses this exact timeline to argue for delaying a construction project by forty-five minutes. Other agents verify this claim by calling `get_wind_conditions` and `get_precipitation_probability`. They debate the combined risk factors before presenting a final, unified recommendation to the user.

Resolve Scheduling Conflicts

`get_daily_forecast` provides the baseline for agents planning multi-day events. The financial agent calculates costs while the weather agent flags potential rain delays. They negotiate the trade-offs of moving dates. By checking `get_aqi_info` for the following week, the health agent ensures the selected timeline avoids severe smog conditions.

Setup guide

Set up Caiyun Weather / 彩云天气 MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Caiyun Weather / 彩云天气 tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Caiyun Weather / 彩云天气_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Caiyun Weather / 彩云天气 data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Caiyun Weather / 彩云天气_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Caiyun Weather / 彩云天气 data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

Install autogen-ext[mcp] first. Use mcp_server_tools with StreamableHttpServerParams pointing to Vinkius, then pass the resulting list to your AssistantAgent.

You assign the toolset to specific agents based on their role. A dedicated weather agent handles all API calls and reports findings back to the group chat.

The calling agent receives the error string and informs the group. The agents then discuss whether to retry the request or proceed without the meteorological data.

The McpToolAdapter translates the MCP server schema into system prompts. The agents read these descriptions and debate which endpoint fits the current problem best.

Your GPS coordinates hit a locked-down V8 isolate that executes the query and immediately terminates. Vinkius retains zero logs of the cities or coordinates your multi-agent system monitors.

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