How to Use the Electricity Maps Carbon Intelligence MCP in CrewAI

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Connect Electricity Maps Carbon Intelligence MCP to CrewAI

Create your Vinkius account to connect Electricity Maps Carbon Intelligence 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 Electricity Maps Carbon Intelligence with CrewAI

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Coordinate regional grid analysis using CrewAI

The `list_energy_zones` tool allows your research agent to discover and catalog all active grid regions. This agent then shares the list of zones with other specialized agents in your crew. By dividing the work, one agent can map out the coordinates while another prepares to analyze the carbon metrics. This collaborative approach makes regional grid discovery highly efficient.

Delegate real-time emissions monitoring

The `get_carbon_intensity` tool retrieves current emissions data for the zones identified by your research agent. An analyst agent uses this tool to evaluate which regions are running cleanest. Right. So. The agent can then pass these metrics to an execution agent. This allows your crew to autonomously decide where to dispatch workloads based on actual grid carbon intensity.

Audit fuel mixes with an autonomous MCP Server crew

The `get_power_production_breakdown` tool details the exact fuel mix currently active on a specific grid. Your auditor agent calls this tool to verify the percentage of wind, solar, and fossil fuels. This breakdown is shared across the crew's memory. The crew can then compile detailed carbon intelligence reports or trigger alerts if renewable generation drops below your targets.

Setup guide

Set up Electricity Maps Carbon Intelligence 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 Electricity Maps Carbon Intelligence tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Electricity Maps Carbon Intelligence 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 Electricity Maps. 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 Electricity Maps Carbon Intelligence MCP in CrewAI

You can pass the server's HTTP URL directly into the agent's parameters during initialization. CrewAI automatically discovers and registers the available tools.

Yes, CrewAI agents share access to the registered tools. The research agent can list zones while the analyst agent checks carbon intensity in parallel.

The server handles requests sequentially or concurrently depending on your setup. You can use CrewAI's tool filters to limit which agents have access to prevent rate limit exhaustion.

No, CrewAI natively supports MCP connections over HTTP and SSE. You do not need to write any custom boilerplate or Python wrappers to fetch grid metrics.

The MCP Server only receives the zone IDs and coordinate strings required to fetch grid data. All queries are handled in a zero-trust, ephemeral sandbox that stores zero transactional data.

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