How to Use the Tomorrow.io Plus MCP in CrewAI

Q: How can Tomorrow.io Plus manage alerts with CrewAI?

You assign an agent to call listalerts. Once that tool returns active warnings, a second agent takes over, using the data to classify which weather events (listevents) are most relevant for immediate action.

Q: Can Tomorrow.io Plus list available location types with CrewAI?

Yes. One agent can run listlocations to gather all known areas. This initial data set is then passed to subsequent agents who use the names in other tools, like checking specific forecasts.

Q: Does tomorrow.io plus support listing all account insights with CrewAI?

A specialized agent can use listinsights to gather the entire catalog of available insight categories. This allows subsequent agents to filter and focus their analysis on specific types of weather data.

Build autonomous weather monitoring teams with CrewAI.

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Connect Tomorrow.io Plus MCP to CrewAI

Create your Vinkius account to connect Tomorrow.io Plus to CrewAI 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 Plus with CrewAI

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Monitor Forecasts Autonomously

Set up a crew where one agent calls `get_forecast_weather` for the coming week, and another agent takes that raw data to analyze patterns. The shared memory lets them pass results back and forth. This simulates a real team: Agent A gathers the bulk forecast; Agent B interprets what those forecasts mean in terms of risk.

Coordinate Event Response

A dedicated agent can run `list_alerts` to check for active weather warnings. If alerts are found, a second specialized agent runs `get_realtime_weather` for the specific impacted zone. This autonomous sequence means that as soon as an alert is detected, the system immediately pulls current conditions without human intervention.

Analyze Complex GeoJSON Areas

You can assign a primary agent to use `post_timelines` with complex boundary data (GeoJSON). This gives the crew an initial analysis of the area's weather profile. A secondary moderator agent then reviews the output, possibly calling `list_insights` to provide context or classification for the findings.

Setup guide

Set up Tomorrow.io Plus MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Tomorrow.io Plus tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Tomorrow.io Plus Analyst",
    goal="Access and analyze Tomorrow.io Plus data via MCP.",
    backstory="Expert analyst with direct Tomorrow.io Plus access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Tomorrow.io Plus transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Tomorrow.io Plus Analyst",
        goal="Access and analyze Tomorrow.io Plus data via MCP.",
        backstory="Expert analyst with direct Tomorrow.io Plus access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Tomorrow.io Plus transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 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 Plus MCP in CrewAI

You assign an agent to call `list_alerts`. Once that tool returns active warnings, a second agent takes over, using the data to classify which weather events (`list_events`) are most relevant for immediate action.

Yes. One agent can run `list_locations` to gather all known areas. This initial data set is then passed to subsequent agents who use the names in other tools, like checking specific forecasts.

Use this when you need continuous operations—for instance, running a full weather audit that checks history (`get_recent_history_weather`), current conditions, and alerts in one automated pipeline.

A specialized agent can use `list_insights` to gather the entire catalog of available insight categories. This allows subsequent agents to filter and focus their analysis on specific types of weather data.

This server touches location coordinates and weather condition data (e.g., temperature, precipitationIntensity). Since the operation is autonomous, you control exactly which agents handle this sensitive information.

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