How to Use the World Bank Full Access MCP in AutoGen

Q: What kind of World Bank Full Access indicators can AutoGen process?

It processes everything from financial data like getfdi to social metrics like getlifeexpectancy. The multi-agent setup forces deep comparison across all 29,506 indicators.

Drive decisions with AutoGen and World Bank Full Access data debate.

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Connect World Bank Full Access MCP to AutoGen

Create your Vinkius account to connect World Bank Full Access 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 Full Access with AutoGen

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Perplexity

Debating policy recommendations

Multiple agents can discuss complex scenarios involving the server. One agent might call `get_gdp` to establish a baseline, while another calls `get_poverty` and challenges that result with data from `get_health_expenditure`. The debate converges on actionable insights. This consensus-driven approach is ideal for policy work. Instead of getting one answer, you get a debated conclusion based on conflicting metrics like comparing the `get_exports` value against the national income level.

Risk assessment via multi-agent debate

Set up competing agents to flag risks using different tools. One agent checks `get_external_debt`, while another monitors `get_inflation`. They will argue over whether the debt risk is exacerbated by rising costs, providing a nuanced, debated answer. This process forces deliberation. The system doesn't just report; it argues why one metric (like `get_unemployment_rate`) outweighs another in a given context.

Comparing development paths

You can assign agents to compare different types of development using the tools. One agent gathers data on education (`get_literacy_rate`), while another pulls environmental metrics like `get_forest_area`. The discussion points out gaps in policy coverage. This is a powerful way to model alternatives. Agents debate whether focusing on clean energy (`get_renewable_energy`) or human capital improvement yields better results.

Setup guide

Set up World Bank Full Access 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 Full Access 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 Full Access_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about World Bank Full Access MCP in AutoGen

Agents use the tools to gather conflicting data points—for instance, comparing high GDP growth (`get_gdp_growth`) against low women in parliament representation (`get_women_in_parliament`). They debate which factor is limiting development.

It processes everything from financial data like `get_fdi` to social metrics like `get_life_expectancy`. The multi-agent setup forces deep comparison across all 29,506 indicators.

Yes. You can construct a scenario where agents debate the optimal path based on current data, such as deciding if improving `get_internet_usage` is more critical than reducing `get_poverty`.

The tools provide annual metrics (like GDP growth or inflation), allowing agents to debate trends over time, simulating policy changes based on past performance data.

This server touches economic indicators, including Gross Domestic Product (GDP), total external debt stocks (% of GNI), and population metrics like the total labor force count.

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