How to Use the Weather (Open-Meteo) MCP in AutoGen

Q: Can AutoGen handle multi-day forecasts using Weather (Open-Meteo)?

Yes. One agent can pull the 1–14 day data via weather.forecast, and another agent reviews that long timeline for patterns or changes in weather systems.

Run debates on complex decisions using AutoGen and the Weather (Open-Meteo) MCP Server.

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Connect Weather (Open-Meteo) MCP to AutoGen

Create your Vinkius account to connect Weather (Open-Meteo) 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 Weather (Open-Meteo) with AutoGen

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Weather (Open-Meteo): Consensus on City Choice

The core function is `weather.compare`. When a user asks, 'Should I go to Lisbon or Madrid?', you can set up two agents: one that analyzes the current weather and another that focuses solely on comfort scores. They debate which city wins. The final decision isn't just based on calling `weather.compare`; it's reached through their negotiation process, giving a highly reasoned answer.

Debating Optimal Outdoor Times with AutoGen

Need to plan the perfect picnic? Have one agent check `weather.best_time` for general activities, and another agent critique that plan by checking `weather.air_quality`. They argue until they converge on a safe, optimal 3-hour window. The result isn't just data; it’s a consensus recommendation derived from multiple perspectives.

Multi-Agent Analysis of Weather (Open-Meteo) Alerts

You can deploy a 'Risk Assessment Agent' and a 'Planning Agent.' The Planning Agent calls `weather.alerts` to check for advisories, while the Risk Agent analyzes the 7-day forecast results from `weather.forecast`. They debate if the alerts are severe enough to cancel the trip. The MCP Server facilitates this discussion by providing structured alert data that both agents can challenge.

Setup guide

Set up Weather (Open-Meteo) 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 Weather (Open-Meteo) 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="Weather (Open-Meteo)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Weather (Open-Meteo) 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="Weather (Open-Meteo)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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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 Open-Meteo. 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 Weather (Open-Meteo) MCP in AutoGen

You build a system where multiple agents debate the answer. For example, one agent checks `weather.current` while another analyzes the air quality from `weather.air_quality`, and they argue about which metric is more important.

Yes. One agent can pull the 1–14 day data via `weather.forecast`, and another agent reviews that long timeline for patterns or changes in weather systems.

You can set up a debate between two agents: one arguing for City A's comfort score and the other arguing for City B. The system then generates a nuanced answer summarizing their disagreement.

No. As long as you pass the tool list from the MCP Server, the `AutoGen` framework handles schema conversion automatically, letting agents focus on the debate logic.

This server provides both quantitative measurements (like PM2.5 levels and temperature ranges) and qualitative status reports, such as health recommendations derived from the AQI scale.

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