How to Use the Xweather Renewable MCP in CrewAI

Q: How does Xweather Renewable assist with planning using CrewAI?

You can assign a dedicated agent to use getextendedforecast to pull the 15-day outlook. Another specialized agent then cross-references that forecast with local station data, running a full risk assessment.

Q: What if Xweather Renewable data is needed for compliance reports using CrewAI?

The crew can run a dedicated audit. One agent pulls gethistoricalobservations, and the moderator agent ensures all necessary metadata from the MCP Server are included in the final report.

Q: What kind of locations does Xweather Renewable support with CrewAI?

The crew starts by using searchlocations to find place details, including coordinates and station metadata. This makes sure every other specialized tool call is grounded in accurate geography.

Run autonomous energy operations with CrewAI's multi-agent specialized workflow.

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Connect Xweather Renewable MCP to CrewAI

Create your Vinkius account to connect Xweather Renewable 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 Xweather Renewable with CrewAI

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Autonomous Energy Analysis

Need a full analysis of farm performance? You set up three agents: Agent A gets the forecast using `get_renewable_energy_farm_data`. Agent B checks solar viability via `get_solar_irradiance_data`. Agent C compiles all findings into an actionable report. The shared memory ensures that Agent C always has access to the raw data pulled by Agents A and B, keeping the operation organized.

Multi-Step Site Validation

Building a new wind farm? The crew handles this. First, a research agent uses `search_locations` for coordinates. Then, an analysis agent runs `get_wind_data`. Finally, the action agent checks local alerts using `get_weather_alerts` to confirm safety. This sequential and collaborative process is what makes CrewAI powerful—it’s not just one tool call.

Historical Data Auditing

For regulatory compliance, you run an audit. One agent pulls `get_historical_observations`, while another uses the data to calculate model deviations. A third monitor agent validates the results against current best practices. The MCP Server feeds raw historical weather observations needed for deep validation work.

Setup guide

Set up Xweather Renewable 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 Xweather Renewable tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Xweather Renewable transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

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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="Xweather Renewable Analyst",
        goal="Access and analyze Xweather Renewable data via MCP.",
        backstory="Expert analyst with direct Xweather Renewable access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Xweather Renewable 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 Vaisala Xweather. 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 Xweather Renewable MCP in CrewAI

You can assign a dedicated agent to use `get_extended_forecast` to pull the 15-day outlook. Another specialized agent then cross-references that forecast with local station data, running a full risk assessment.

Yes. You give one agent the role of 'Wind Assessor.' That agent uses `get_wind_data` and coordinates with another agent that checks nearby stations using `get_closest_weather_station`.

The crew can run a dedicated audit. One agent pulls `get_historical_observations`, and the moderator agent ensures all necessary metadata from the MCP Server are included in the final report.

The crew starts by using `search_locations` to find place details, including coordinates and station metadata. This makes sure every other specialized tool call is grounded in accurate geography.

It touches weather observations and energy production metrics. Specific types include historical irradiance, current conditions, wind speed/direction, and farm power output.

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