How to Use the World Bank Economy MCP in LlamaIndex

Q: How does LlamaIndex improve World Bank Economy analysis?

LlamaIndex indexes the raw data returned by tools like getgdp, turning single API calls into searchable knowledge. This lets your application answer complex questions grounded in real World Bank Economy figures.

Q: Can I query past records of World Bank Economy indicators using LlamaIndex?

Yes. By indexing the results from getinflation and other tools, you build a historical knowledge base. You can then query this index to retrieve answers based on data points from previous sessions.

Build knowledge-augmented RAG applications with LlamaIndex on World Bank Economy data.

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Connect World Bank Economy MCP to LlamaIndex

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

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Indexing World Bank Economy Metrics

The `get_gdp` tool provides current US$ GDP figures for various economies. With LlamaIndex, you don't just call this API; the resulting data point becomes part of a searchable index. You can then query past sessions or documents against that live economic fact. This means your RAG application grounds answers in actual World Bank Economy data, eliminating hallucinations and letting users search across both their own knowledge base and live metrics.

Semantic Search for MCP Server Data

Instead of just getting a single inflation rate from `get_inflation`, you index it. Later, if a user asks about 'periods of high price volatility in the 2010s,' LlamaIndex searches your knowledge base and retrieves that specific World Bank Economy data point alongside related documents. The MCP Server output becomes part of a unified, queryable index, making live economic data searchable by meaning, not just keyword.

Combining Indicators with LlamaIndex

You can combine the results from `get_gdp_growth` and `get_economy_indicator`. By indexing both sets of metrics together, you build a holistic knowledge base. A user's query might reference 'slow growth countries,' and the index pulls relevant data from both tools. This allows your FunctionAgent to combine live API data with stored documents for highly accurate answers.

Setup guide

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

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Common questions about World Bank Economy MCP in LlamaIndex

LlamaIndex indexes the raw data returned by tools like `get_gdp`, turning single API calls into searchable knowledge. This lets your application answer complex questions grounded in real World Bank Economy figures.

Yes. By indexing the results from `get_inflation` and other tools, you build a historical knowledge base. You can then query this index to retrieve answers based on data points from previous sessions.

It touches financial sector indicators, which are primarily represented as currency values (US$) for GDP and floating-point numbers for percentages like inflation. The core data type is the economic indicator value.

It merges them into a single index. Live API calls update the knowledge base, while documents provide historical context. This unified view lets your FunctionAgent answer questions that require both types of information.

Yes. When setting up the tools, you can use filters and include resources to ensure the FunctionAgent only accesses or considers specific subsets of the World Bank Economy metrics you need.

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