How to Use the MeteoSource MCP in AutoGen

Q: How do I register MeteoSource tools with an AutoGen agent using MCP?

Install autogen-extmcp, then use mcpservertools with your Vinkius server URL. Pass the generated tools list directly into the AssistantAgent constructor.

Q: How do AutoGen agents handle MeteoSource API errors?

If a tool call like getpointforecast fails, the error message is returned to the agent conversation. The agents can read the error and decide whether to retry or use a fallback.

Q: Can AutoGen agents handle timezone-sensitive scheduling?

Yes, the agents use getplacetimezone to coordinate schedules. This ensures that a local weather forecast is correctly mapped to the local time of the target asset.

Let AutoGen agents debate and verify MeteoSource weather data to make consensus-driven operational decisions.

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Setup MeteoSource with AutoGen

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Multi-agent weather debate via AutoGen

`get_point_forecast` feeds raw atmospheric data directly into your AutoGen agent conversations using the MCP standard. One AutoGen agent can pull the forecast, while a second agent challenges the accuracy or checks it against historical thresholds. This collaborative approach ensures that your AutoGen system does not act on a single unverified MeteoSource forecast. The debate happens entirely within the AutoGen framework, using live weather values to reach a consensus.

Coordinate resolution using AutoGen agents

`get_nearest_weather_place` resolves GPS coordinates into verified place IDs during multi-agent AutoGen discussions. When your dispatch agent passes latitude and longitude, another AutoGen agent runs this tool to identify the nearest weather station. This prevents coordinate mismatches during complex AutoGen routing operations. The agents verify the resolved MeteoSource place ID together, ensuring they are both discussing the exact same geographic point before querying forecasts.

Active MCP Server monitoring in AutoGen

`check_api_status` confirms that the weather service is online before your AutoGen agents start a planning session. A supervisor AutoGen agent runs this tool to make sure the MeteoSource data source is healthy. This avoids infinite loops where AutoGen agents repeatedly try to fetch forecasts from a failing endpoint. It keeps your multi-agent conversations focused and computationally efficient.

Setup guide

Set up MeteoSource 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 MeteoSource 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="MeteoSource_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent MeteoSource data")
print(result.messages[-1].content)

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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="MeteoSource_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent MeteoSource 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 MeteoSource. 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 MeteoSource MCP in AutoGen

Install `autogen-ext[mcp]`, then use `mcp_server_tools` with your Vinkius server URL. Pass the generated tools list directly into the `AssistantAgent` constructor.

Yes, you can share the tools among different agents in your group chat. One agent might resolve the location using `search_weather_places` while another fetches the forecast.

If a tool call like `get_point_forecast` fails, the error message is returned to the agent conversation. The agents can read the error and decide whether to retry or use a fallback.

Yes, the agents use `get_place_timezone` to coordinate schedules. This ensures that a local weather forecast is correctly mapped to the local time of the target asset.

No, Vinkius processes your coordinate and place ID queries in a secure, ephemeral sandbox on our MCP host. The data is never written to persistent logs, ensuring your operational coordinates remain confidential.

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