How to Use the AerisWeather MCP in AutoGen

Q: How do AutoGen agents share AerisWeather observations during a debate?

One agent executes the getobservations tool and posts the resulting weather metrics to the group chat. The other AutoGen agents then read this shared context to adjust their respective plans.

Q: Can an AutoGen safety agent trigger alerts using AerisWeather?

Yes, you can program a safety agent to call getalerts at regular intervals. If an active weather advisory is found, the agent broadcasts the warning to the rest of the conversation group.

Q: How does the AerisWeather MCP Server handle multi-agent batch requests in AutoGen?

A coordinator agent can call the getbatch tool to fetch forecasts, alerts, and conditions simultaneously. This single payload is then distributed to the specialized agents in the AutoGen group, saving API calls.

Let your AutoGen agents debate and coordinate weather-dependent decisions with live AerisWeather data.

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Setup AerisWeather with AutoGen

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Resolve weather debates using `get_observations`

The `get_observations` tool provides the ground-truth weather metrics your AutoGen agents need to settle multi-agent debates. When a logistics agent and a safety agent argue over routing, they call this tool to fetch current wind speed and visibility. This direct data access prevents agents from hallucinating environmental conditions during their planning phase. By using real-time observations, your conversational team arrives at a consensus based on actual physical data.

Coordinate safety checks with the AutoGen MCP Server tools

The `get_alerts` tool feeds active weather warnings and watches directly into your AutoGen safety agents. A specialized monitor agent can continuously query this tool to alert other agents about incoming storm systems or hazardous conditions. Once an alert is detected, the agents debate the severity of the advisory before taking action. This multi-agent verification loop ensures your automated workflows only pause when a warning is verified.

Batch multi-agent data requests with `get_batch`

The `get_batch` tool allows your AutoGen agents to query up to 31 distinct weather endpoints in a single conversational turn through the MCP Server. Instead of multiple agents making individual calls, one coordinator agent gathers forecasts, alerts, and observations at once. This batching mechanism reduces token usage and prevents communication bottlenecks between your agents. Your system stays highly responsive even when multiple agents require complex environmental data simultaneously.

Setup guide

Set up AerisWeather 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 AerisWeather 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="AerisWeather_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent AerisWeather 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="AerisWeather_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

One agent executes the `get_observations` tool and posts the resulting weather metrics to the group chat. The other AutoGen agents then read this shared context to adjust their respective plans.

Yes, you can program a safety agent to call `get_alerts` at regular intervals. If an active weather advisory is found, the agent broadcasts the warning to the rest of the conversation group.

A coordinator agent can call the `get_batch` tool to fetch forecasts, alerts, and conditions simultaneously. This single payload is then distributed to the specialized agents in the AutoGen group, saving API calls.

Your agents can query historical conditions back to 2004 using `get_conditions`. This allows them to debate whether current weather patterns are anomalous compared to historical baselines.

Yes, Vinkius routing runs on a zero-trust model where all location queries and weather coordinates remain ephemeral. No data passing through the MCP Server is stored, cached, or used for model training.

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