How to Use the AerisWeather MCP in CrewAI

Q: How do CrewAI agents handle batch AerisWeather lookups?

Your lead agent can call getbatch to query observations and forecasts for up to 31 locations at once, saving API tokens and execution time.

Equip your CrewAI agent teams with AerisWeather tools to automate complex agricultural and logistics decisions.

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Connect AerisWeather MCP to CrewAI

Create your Vinkius account to connect AerisWeather 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 AerisWeather with CrewAI

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Perplexity

Coordinate weather monitoring using specialized CrewAI teams

Running a complex logistics operation requires multiple perspectives. One agent can monitor region-wide conditions with `get_alerts` while a supervisor agent decides how to reroute vehicles. This MCP Server feeds clean data into their shared memory. The agents collaborate in real-time, passing historical data and active warnings back and forth to build a unified plan.

Analyze historical and current trends autonomously

Give your analyst agent the ability to compare past conditions with current trends. The agent calls `get_observations` to pull current metrics and uses `get_forecasts` to project future impacts. Because CrewAI supports hierarchical execution, a manager agent can review these findings. The manager agent then decides whether to trigger a deeper batch query using `get_batch`.

Resolve complex geographical locations across agent tasks

Agents often struggle to match messy user input to exact coordinates. Your research agent can use this MCP tool to resolve city names and airport codes into precise geographical targets. Once resolved, the research agent hands the clean location ID to the weather agent. The weather agent then calls `get_conditions` to check for minutely precipitation trends.

Setup guide

Set up AerisWeather 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 AerisWeather tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent AerisWeather 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="AerisWeather Analyst",
        goal="Access and analyze AerisWeather data via MCP.",
        backstory="Expert analyst with direct AerisWeather access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent AerisWeather 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 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 CrewAI

Install the package using pip, then pass the MCP Server URL directly into your agent's mcps list. The framework configures the tools automatically.

Yes. You can assign the server to the entire crew or restrict specific tools like get_alerts to your monitoring agent while others use get_places.

Your lead agent can call get_batch to query observations and forecasts for up to 31 locations at once, saving API tokens and execution time.

Yes. A manager agent can coordinate tasks, instructing a subordinate agent to fetch data via get_observations before running analytical models on the results.

All weather queries and geographic coordinates are routed through isolated MCP sandboxes. No operational data is retained or stored after the crew finishes its run.

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