How to Use the World Bank Countries MCP in LlamaIndex

Q: Does LlamaIndex support querying World Bank Countries data for different regions?

Yes. By indexing the results of searchregions, you allow your agent to query specific geographic areas semantically, making it useful even if the user's prompt is vague.

Ground your AI client's answers in World Bank Countries data with LlamaIndex indexing.

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Connect World Bank Countries MCP to LlamaIndex

Create your Vinkius account to connect World Bank Countries 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 Countries with LlamaIndex

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Indexing country lists for RAG

The `list_countries` tool output becomes part of a searchable knowledge base when you index it. Instead of needing the API live, your agent queries the stored data to find specific countries or their attributes. This means you can build an application that references past World Bank Countries metadata without hitting rate limits—the answer is grounded in the indexed data.

Semantic search on income levels

You don't just call `search_income_levels` once; you index its results. This lets your agent semantically query past metadata, allowing it to find countries matching a description (e.g., 'low-income nations') even if the exact code isn't used in the prompt. The result is that live API data and document knowledge combine into one unified, queryable index for better context retrieval.

Combining regions and metadata

LlamaIndex lets you index multiple sources. You can mix the results of `search_regions` with internal documents. Your agent then answers a question by combining the geographic rules from the MCP Server with proprietary knowledge. This eliminates guesswork, providing concrete answers that cite both the World Bank Countries metadata and your own stored information.

Setup guide

Set up World Bank Countries 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 Countries 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 Countries tools.",
)
response = await agent.run("List recent World Bank Countries data")

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Common questions about World Bank Countries MCP in LlamaIndex

It indexes the API outputs (like country lists or regions) into a vector store. This lets your agent perform semantic search on historical metadata, retrieving answers grounded in actual data.

You can index the structured classifications: country ISO codes, global income levels (HIC/LIC), and recognized geographic regions. This turns API results into searchable knowledge.

You index the MCP Server's output alongside your documents. Your agent then combines both sources to answer queries, ensuring the metadata is accurate and context-aware.

Yes. By indexing the results of `search_regions`, you allow your agent to query specific geographic areas semantically, making it useful even if the user's prompt is vague.

This server touches structured metadata: country ISO codes, geographic regions, and global income/lending classifications. Since this data is public classification standards, indexing it doesn't introduce any private user risk.

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