How to Use the Tomorrow.io MCP in AutoGen

Q: Can AutoGen use Tomorrow.io for immediate decision support?

Yes. Agents can pull getrealtimeweather and instantly debate the optimal course of action based on current conditions (lat/lon/city name) versus the desired goal.

Q: Does Tomorrow.io help AutoGen with complex risk scenarios?

It handles this through tools like getroadweatherrisk and getwildfirerisk. Agents can debate which hazard is more pressing, leading to a prioritized safety conclusion.

Facilitate consensus on climate decisions with the AutoGen MCP Server.

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Connect Tomorrow.io MCP to AutoGen

Create your Vinkius account to connect Tomorrow.io 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 Tomorrow.io with AutoGen

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Debating Forecast Scenarios

Use `get_daily_forecast` to establish a baseline prediction. You can then set up an agent debate: one agent argues for travel based on the forecast, while another checks it against `get_hourly_forecast`'s detailed warnings. They negotiate which time window presents the lowest risk, converging on a single best recommendation.

Multi-Agent Risk Negotiation

Set up two agents: a Travel Agent and a Safety Agent. The Travel Agent pulls `get_realtime_weather` for speed, but the Safety Agent checks `get_road_weather_risk`. They debate if the current conditions justify the travel plan. This simulates real-world decision-making where competing priorities must be weighed.

Climate Policy Deliberation

You can run a multi-agent system to assess long-term impact. One agent uses `get_historical_weather` to check past extremes, while another checks the current `get_air_quality_index`. They debate whether the location needs preventative measures. The output is not just data; it's a consensus report on environmental necessity.

Setup guide

Set up Tomorrow.io 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 Tomorrow.io 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="Tomorrow.io_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You feed the tool outputs into competing agents. For instance, one agent can query `get_pollen_forecast` while another checks the `get_daily_forecast`. The debate then revolves around reconciling those two different views of environmental risk.

It provides highly specific, actionable climate intelligence—everything from minute-by-minute precipitation to wildfire risk indices. This allows agents to challenge each other with concrete, measurable facts.

Yes. Agents can pull `get_realtime_weather` and instantly debate the optimal course of action based on current conditions (lat/lon/city name) versus the desired goal.

It handles this through tools like `get_road_weather_risk` and `get_wildfire_risk`. Agents can debate which hazard is more pressing, leading to a prioritized safety conclusion.

The server provides diverse metrics including air quality indices and temperature extremes. This variety allows the agents to argue from multiple scientific perspectives simultaneously.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript