How to Use the World Bank Economy MCP in CrewAI

Q: Can I use World Bank Economy to predict economic outcomes with CrewAI?

The agents can analyze current metrics (like using getgdpgrowth and getinflation) and generate potential scenarios. However, the prediction itself is based on pattern recognition from the data provided by the MCP Server.

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Connect World Bank Economy MCP to CrewAI

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

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Claude

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Automated Economic Research

You set up specialized agents for analyzing economic data. One agent researches the market using `get_economy_indicator`, while a second analyzes the findings by fetching current GDP figures via `get_gdp`. The crew executes these roles autonomously, passing shared memory from research to analysis without needing human intervention or manual tool calling.

Analyze Growth and Inflation Trends

Need a full economic picture? Assign one agent to monitor `get_gdp_growth` and another to track inflation with `get_inflation`. The moderator agent then synthesizes these two streams of data into a coherent report. This sequential, collaborative approach lets you build sophisticated operations that synthesize multiple World Bank Economy metrics automatically.

Structured Data Reporting

The crew works together to structure raw data. One agent calls `get_economy_indicator` for a list of codes; another formats the results into a markdown table. The process is fully autonomous. This specialized role-based specialization means you don't just get data; you get actionable, pre-formatted intelligence.

Setup guide

Set up World Bank Economy 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 Economy tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent World Bank Economy 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 Economy MCP in CrewAI

You assign the MCP Server as a tool set to your agents. The crew then decides which agent needs to call `get_gdp` or another tool, allowing for specialized, collaborative data retrieval.

The server deals with macro-level financial metrics: GDP figures, inflation percentages, and any specific indicator you query via `get_economy_indicator`. These are the core inputs for your research crew.

The agents can analyze current metrics (like using `get_gdp_growth` and `get_inflation`) and generate potential scenarios. However, the prediction itself is based on pattern recognition from the data provided by the MCP Server.

Yes. You can build a crew where one agent systematically calls `get_gdp` and `get_inflation` for several different country codes, compiling a comparative report.

The server touches public economic indicators (GDP values, inflation rates). Since your crew operates on aggregate and global financial data, there are no personal identifiers involved.

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