How to Use the World Bank Full Access MCP in CrewAI

Q: How does CrewAI use World Bank Full Access for research?

You assign roles. One agent is tasked with searching (searchindicators), and another processes the results, calling specific metrics like gettotalpopulation to provide context to the findings.

Q: What kind of economic data can World Bank Full Access feed into CrewAI?

The server provides a massive catalog including GDP (getgdp), inflation rates (getinflation), and labor force counts (getlaborforce). The agents work off these specific, measurable metrics.

Q: Can World Bank Full Access support complex region-wide studies for CrewAI?

Yes. You use listtopics or searchregions to narrow the focus, allowing your agent crew to run comparative analysis on a continent or global group.

Autonomous Global Research: CrewAI and World Bank Full Access Agents.

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Connect World Bank Full Access MCP to CrewAI

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

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Collaborative Economic Analysis with MCP Server

Set up a research crew where one agent calls `get_gdp` to establish the economic baseline. A second specialized agent then uses that data to analyze trade metrics by calling `get_exports`. The third agent compares this against foreign direct investment (`get_fdi`) to draw conclusions. The MCP Server ensures the agents share memory, so they don't repeat calls or lose context. This is crucial for building detailed reports about global trade dynamics.

Environmental Impact Assessment using World Bank Full Access

Build a dedicated 'Environment Agent.' First, it runs `get_co2_emissions` to get the per capita metric tons. Next, another agent pulls data on renewable energy consumption (`get_renewable_energy`) and forest area (`get_forest_area`). The agents can then compare these two datasets—showing where a country has high emissions but low documented green energy use—generating actionable insights.

Human Development Index Calculation via CrewAI

You define a 'Development Agent.' This agent pulls diverse data points: `get_life_expectancy`, `get_literacy_rate`, and the poverty headcount ratio (`get_poverty`). The collective crew then analyzes these three streams together. The ability to query any World Bank indicator via `get_indicator_data` means you can customize the exact metrics needed for specialized, multi-faceted reports.

Setup guide

Set up World Bank Full Access 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 Full Access tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent World Bank Full Access 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 →

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Common questions about World Bank Full Access MCP in CrewAI

You assign roles. One agent is tasked with searching (`search_indicators`), and another processes the results, calling specific metrics like `get_total_population` to provide context to the findings.

The server provides a massive catalog including GDP (`get_gdp`), inflation rates (`get_inflation`), and labor force counts (`get_labor_force`). The agents work off these specific, measurable metrics.

Yes. You use `list_topics` or `search_regions` to narrow the focus, allowing your agent crew to run comparative analysis on a continent or global group.

The system allows specialized agents to call tools sequentially. For example, Agent A gets `get_gdp_growth`, and Agent B uses that figure to determine if debt management is required.

This server touches economic metrics, specifically Gross Domestic Product (GDP) in current US$.

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