How to Use the World Bank Countries MCP in CrewAI

Run specialized teams to analyze World Bank Countries metadata with CrewAI.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect World Bank Countries MCP to CrewAI

Create your Vinkius account to connect World Bank Countries 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.

GDPR Free for Subscribers

Setup World Bank Countries with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Research country lists using the MCP Server.

You define Agent A to run `list_countries`. This agent researches all available nations, and its findings are stored in shared memory for subsequent agents. The system ensures that every specialized role—like a Research Specialist or an Analyst—has access to this foundational country data.

Analyze World Bank Countries by income levels.

You can assign Agent B the task of analyzing economic tiers using `search_income_levels`. This agent filters and analyzes countries like g, HIC, or LIC. The monitor agent then reviews these findings, ensuring that only data relevant to the project's goals is passed forward.

Map World Bank Countries by geographic regions.

Agent C uses `search_regions` to map countries by their location. This allows your crew to quickly segment a global dataset into manageable groups. The hierarchical execution pattern means that Agent C's regional output feeds directly into the final report generated by the moderator agent.

Setup guide

Set up World Bank Countries 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 Countries tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent World Bank Countries 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect World Bank Countries now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about World Bank Countries MCP in CrewAI

It uses specialized agents to break down complex tasks. One agent researches the country list, another analyzes income levels, and a third maps regions—all within a collaborative session.

Yes. The crew model is designed for autonomy. You set up the roles, define the tools (like `list_countries`), and the moderator agent runs the whole sequence without human intervention.

It handles geographic metadata: ISO codes, regional classifications, and global income levels. These structured strings are shared across all specialized agents in your crew.

Absolutely. You can define roles for multiple tools—for instance, a combination of `search_regions` and `search_income_levels` run sequentially by different agents.

The shared memory mechanism is key. All agents operate on the same, validated dataset of country metadata and region classifications throughout the entire operation.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript