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

Q: Can AutoGen compare emissions data with World Bank Climate & Energy?

Yes. You can set up two competing agents—one analyzing getco2emissions and another checking indicator trends—to force a debate on which metric is the best primary focus for policy.

Q: What data types are available in the MCP Server?

It covers indicators, including forest area percentages (getforestarea), electricity access rates, and renewable energy consumption shares.

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Connect World Bank Climate & Energy MCP to AutoGen

Create your Vinkius account to connect World Bank Climate & Energy to AutoGen 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 AutoGen

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Debating Climate Policy with AutoGen

AutoGen doesn't just call tools; it makes agents debate the best course of action. You can set up a system where one agent uses `get_forest_area` to assess deforestation risk, and another agent challenges that conclusion by citing data from `get_climate_indicator`. The final decision emerges from their negotiation. This is ideal for systems where there's no obvious answer—the solution requires competing perspectives to converge on a single recommendation.

Analyzing Energy Trade-offs with the MCP Server

Need to weigh different energy sources? Set up two agents: one focused on cost (using `get_electricity_access`) and another focused on sustainability (using `get_renewable_energy`). They will argue over the optimal mix of power generation, using the World Bank Climate & Energy MCP Server's tools. The resulting consensus provides a nuanced recommendation that weighs multiple conflicting technical factors.

Modeling Global Emissions Scenarios

You can build an agent team to simulate scenarios. One agent gathers the current emissions via `get_co2_emissions`. A second agent then uses this baseline, along with data from `get_climate_indicator` and `get_forest_area`, to project necessary reductions or changes. The system doesn't just report numbers; it discusses *why* those numbers mean something and what the next logical step should be.

Setup guide

Set up World Bank Climate & Energy MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes World Bank Climate & Energy tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="World Bank Climate & Energy_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent World Bank Climate & Energy data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="World Bank Climate & Energy_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent World Bank Climate & Energy data")
    print(result.messages[-1].content)

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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 AutoGen

It provides the necessary factual tools for your agents to debate over. Instead of just getting data, you're using the data points (like CO2 emissions) as evidence in a multi-agent conversation.

You get consensus-driven policy recommendations. For example, instead of just seeing low electricity access rates, the agents will debate *why* that's a problem and what mitigating factors exist.

Yes. You can set up two competing agents—one analyzing `get_co2_emissions` and another checking indicator trends—to force a debate on which metric is the best primary focus for policy.

It covers indicators, including forest area percentages (`get_forest_area`), electricity access rates, and renewable energy consumption shares.

Absolutely. You can give competing agents the task of assessing climate risk by having them call `get_climate_indicator` alongside `get_co2_emissions` and debate which combination is most critical.

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