How to Use the World Bank Climate & Energy MCP in CrewAI

Q: Can CrewAI handle complex comparisons using World Bank Climate & Energy data?

Yes. You give it a crew: Agent A gathers CO2 emissions (getco2emissions), and Agent B compares that result against forest area data (getforestarea). They work together.

Q: What is the best way to model an energy audit with World Bank Climate & Energy using CrewAI?

You'd set up a crew where one agent focuses on getelectricityaccess and another handles getrenewableenergy. The moderator then reviews both outputs, ensuring nothing was missed.

Run autonomous research teams on World Bank Climate & Energy metrics using CrewAI agents.

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Connect World Bank Climate & Energy MCP to CrewAI

Create your Vinkius account to connect World Bank Climate & Energy 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 World Bank Climate & Energy with CrewAI

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Perplexity

Autonomous Indicator Research

You set up a 'Research Agent' to use `get_climate_indicator`. This agent automatically queries the World Bank data for specified codes. A separate 'Analysis Agent' then processes these results, synthesizing findings without human input. The whole system runs autonomously: research happens, and analysis follows immediately.

Deep Resource Use Analysis

Need to compare CO2 emissions (`get_co2_emissions`) against renewable energy usage? The 'Specialist Agent' handles the data calls. A 'Moderator Agent' then watches both metrics and drafts a comparative summary report. This structured, multi-agent approach makes complex climate comparisons reliable.

Comprehensive Land & Energy Audit

A crew of agents can audit an entire region. One agent checks `get_forest_area` while another uses `get_electricity_access`. The 'Action Agent' then compiles these findings into a single, structured output for you. It’s full automation: the specialized roles handle the data gathering and synthesis.

Setup guide

Set up World Bank Climate & Energy 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 World Bank Climate & Energy tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent World Bank Climate & Energy 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 World Bank Open Data. 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 World Bank Climate & Energy MCP in CrewAI

You define a 'Research Agent' tasked with calling tools like `get_climate_indicator`. This agent executes the calls, and the subsequent agents take over the analysis of those results.

Yes. You give it a crew: Agent A gathers CO2 emissions (`get_co2_emissions`), and Agent B compares that result against forest area data (`get_forest_area`). They work together.

You'd set up a crew where one agent focuses on `get_electricity_access` and another handles `get_renewable_energy`. The moderator then reviews both outputs, ensuring nothing was missed.

Yes. It manages indicator codes for general metrics, percentages for access rates, and metric ton counts for emissions. The agents handle these varied formats automatically.

The system deals with indicator codes, which represent the source of all metrics—whether it's CO2 emissions or electricity access rates.

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