World Bank Countries MCP Server for LlamaIndex 3 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add World Bank Countries as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to World Bank Countries. "
"You have 3 tools available."
),
)
response = await agent.run(
"What tools are available in World Bank Countries?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About World Bank Countries MCP Server
Provide your AI agent with the World Bank's master taxonomy of geography to build deeply accurate filtering and mapping tools.
LlamaIndex agents combine World Bank Countries tool responses with indexed documents for comprehensive, grounded answers. Connect 3 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Country List — Rapidly retrieve massive lists of countries, their exact ISO standards, and capital cities.
- Regions & Lending — Identify macroscopic regional blocks or a country's classification logic.
- Income Taxonomies — Perfect standard groupings (e.g. searching 'HIC' High Income Countries).
The World Bank Countries MCP Server exposes 3 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect World Bank Countries to LlamaIndex via MCP
Follow these steps to integrate the World Bank Countries MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 3 tools from World Bank Countries
Why Use LlamaIndex with the World Bank Countries MCP Server
LlamaIndex provides unique advantages when paired with World Bank Countries through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine World Bank Countries tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain World Bank Countries tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query World Bank Countries, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what World Bank Countries tools were called, what data was returned, and how it influenced the final answer
World Bank Countries + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the World Bank Countries MCP Server delivers measurable value.
Hybrid search: combine World Bank Countries real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query World Bank Countries to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying World Bank Countries for fresh data
Analytical workflows: chain World Bank Countries queries with LlamaIndex's data connectors to build multi-source analytical reports
World Bank Countries MCP Tools for LlamaIndex (3)
These 3 tools become available when you connect World Bank Countries to LlamaIndex via MCP:
list_countries
List World Bank countries
search_income_levels
g., HIC, LIC). List World Bank income levels
search_regions
List World Bank geographic regions
Example Prompts for World Bank Countries in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with World Bank Countries immediately.
"List all high-income countries in East Asia."
"What are the World Bank geographic regions?"
"Show me the income level classification for all South American countries."
Troubleshooting World Bank Countries MCP Server with LlamaIndex
Common issues when connecting World Bank Countries to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpWorld Bank Countries + LlamaIndex FAQ
Common questions about integrating World Bank Countries MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect World Bank Countries with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect World Bank Countries to LlamaIndex
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.