How to Use the World Bank Economy MCP in AutoGen

Q: Can multiple agents compare World Bank Economy indicators using AutoGen?

Yes. You can assign one agent to check getgdp and another to verify the implications against getinflation. They challenge each other's assumptions until a definitive conclusion is reached.

Facilitate consensus on economic predictions using AutoGen with World Bank Economy data.

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Connect World Bank Economy MCP to AutoGen

Create your Vinkius account to connect World Bank Economy 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 Economy with AutoGen

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Multi-Agent Debate on Economic Forecasts

You set up multiple agents—say, a 'Pessimism Agent' and an 'Optimism Agent.' Both use the `get_gdp_growth` tool to pull World Bank Economy data. They then debate whether recent growth rates signal recession or recovery. The user isn't getting one answer; they are getting a negotiation between competing viewpoints, leading to a consensus conclusion.

MCP Server for Structured Economic Deliberation

The MCP Server provides tools like `get_gdp` and `get_inflation`. An agent can be assigned the task of checking if a projected growth rate is sustainable. It calls the tool, passes the result to another agent (the 'Risk Agent'), which then evaluates it based on inflation data. This simulates real-world advisory teams, where decisions require multiple perspectives and checks.

Automating World Bank Economy Data Review

Want to review a complex economic scenario? You can deploy an agent pipeline: one fetches `get_economy_indicator` data for Country A, while another fetches the same data for Country B. They then debate which country's indicators present a more stable investment profile. The result is a fully reasoned analysis that weighs multiple factors and sources of truth.

Setup guide

Set up World Bank Economy 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 Economy 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 Economy_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent World Bank Economy 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 Economy_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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 Economy MCP in AutoGen

AutoGen uses the tools to gather raw metrics, like GDP or inflation. The agents then debate these numbers, forcing a consensus that is more robust than any single API call could provide.

Yes. You can assign one agent to check `get_gdp` and another to verify the implications against `get_inflation`. They challenge each other's assumptions until a definitive conclusion is reached.

It touches financial sector indicators, which are provided as percentage rates for growth and inflation, and absolute currency values (US$) for GDP. The underlying type is the indicator value.

It helps by making the process of modeling conversational. Instead of writing code to calculate risk, you let agents argue about the risks presented by the data points.

The server provides financial sector indicators. The exact data type is the economic indicator value itself—a quantifiable metric of a country's economy.

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