How to Use the World Bank Education & Health MCP in LlamaIndex

Q: How does LlamaIndex handle World Bank Education & Health data?

It takes the structured output from tools like getlifeexpectancy and converts it into semantic embeddings. This allows your agent to understand the meaning of 'low life expectancy' rather than just retrieving a number.

Q: Is this World Bank Education & Health dataset good for RAG applications with LlamaIndex?

Yes. The data points—such as getinfantmortality and geteduhealthindicator—are perfect candidates for indexing because they are factual, measurable metrics that need to be cross-referenced.

Build searchable World Bank Education & Health knowledge bases with LlamaIndex.

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Connect World Bank Education & Health MCP to LlamaIndex

Create your Vinkius account to connect World Bank Education & Health 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 Education & Health with LlamaIndex

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Indexing indicators for search using LlamaIndex

LlamaIndex takes the output from tools like `get_health_expenditure` and indexes it into a vector store. This means you don't just get a number; you create a searchable knowledge chunk tied to that metric. Later, your AI client can query this index—for example, 'What was the health expenditure in 2015?'—and get an answer grounded in the actual World Bank Education & Health data.

Creating RAG systems with LlamaIndex and MCP Server

You combine live API calls with internal documents. For example, you can run `get_literacy_rate` for a country, store the result in the index, and then query that data alongside policy documents. This turns scattered World Bank Education & Health metrics into one unified, queryable knowledge base accessible through your agent.

Comparing global stats with LlamaIndex

Need to check a trend? Use `get_edu_health_indicator` repeatedly and index the results. When you ask a question like 'How did life expectancy change?', the RAG system searches across all indexed data points, giving context. LlamaIndex keeps your World Bank Education & Health metrics searchable for historical comparison, not just single-shot lookups.

Setup guide

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

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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.

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Common questions about World Bank Education & Health MCP in LlamaIndex

It takes the structured output from tools like `get_life_expectancy` and converts it into semantic embeddings. This allows your agent to understand the *meaning* of 'low life expectancy' rather than just retrieving a number.

You can index almost anything: rates, expenditures, indicators. Because you are building a knowledge graph from the MCP Server, the answer is highly contextualized by the surrounding documents.

Yes. The data points—such as `get_infant_mortality` and `get_edu_health_indicator`—are perfect candidates for indexing because they are factual, measurable metrics that need to be cross-referenced.

You can index all of them. The MCP Server exposes five tools covering everything from literacy rates to overall health expenditure, and each one is ready to be added to your vector store.

The server touches global statistics (indicator codes, rates). Since the data retrieved via `get_literacy_rate` is aggregated, there's no concern about handling private or personally identifiable information.

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