How to Use the World Bank Labor & Trade MCP in LlamaIndex

Q: Can I build an index for World Bank Labor & Trade metrics using LlamaIndex?

Yes. You can combine output from getlaborforce with document text into a single, queryable index. This lets you search documents about the labor force and retrieve the actual data point.

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Connect World Bank Labor & Trade MCP to LlamaIndex

Create your Vinkius account to connect World Bank Labor & Trade to LlamaIndex 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 Labor & Trade with LlamaIndex

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Indexing Economic Reports via LlamaIndex

The output from `get_exports` becomes part of your searchable index. You don't just get a number; you ground that data point in a knowledge base, making it retrievable later for semantic search. This means you can query past results and get answers based on actual World Bank API figures. This RAG approach lets developers combine live trade data with internal documents—say, linking high export rates to specific regional policy memos.

Querying Labor Market Trends using LlamaIndex

LlamaIndex turns complex API calls into searchable knowledge. You can index results from `get_unemployment_rate` and use that as context for answering questions about labor market stability. The system retrieves the specific rate and tells you exactly when it was found. This is useful because you're not just running a query; you're building an answer that cites the source data, whether it's from `get_labor_force` or `get_fdi`.

Deep Dive into Indicators with MCP Server

Need to compare diverse indicators? Run a query against `get_labor_trade_indicator`. The result is immediately ingested and indexed, allowing you to search across many different indicator codes without needing a specific tool call every time. It's all part of your unified knowledge index. This capability supports combining data from multiple sources into one coherent answer set, making the MCP Server extremely versatile for economic research.

Setup guide

Set up World Bank Labor & Trade MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all World Bank Labor & Trade MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to World Bank Labor & Trade tools.",
)
response = await agent.run("List recent World Bank Labor & Trade data")

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Common questions about World Bank Labor & Trade MCP in LlamaIndex

You run a query that asks about correlations. The index retrieves the relevant figures—like `get_exports` and `get_fdi`—and grounds them in an answer, showing you where the data came from.

The MCP Server output is converted into a searchable vector store. This means your application can query past API results and policy documents together, getting answers that are both current and contextual.

Yes. You can combine output from `get_labor_force` with document text into a single, queryable index. This lets you search documents *about* the labor force and retrieve the actual data point.

The server handles aggregate economic metrics, including percentages of GDP for FDI, exports, unemployment rates, and labor force totals. No personally identifiable information (PII) is ever touched or indexed.

Yes, because the results are indexed. You can query across multiple time points for indicators like `get_labor_trade_indicator`, making past trends easily searchable.

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