How to Use the Nationalize MCP in LlamaIndex

Q: How do I add the Nationalize tool to my LlamaIndex agent?

First, you initialize the BasicMCPClient pointing to this server's endpoint. Then, you wrap it with McpToolSpec and call totoollistasync() to get a list of tools, including predictnationality, ready for your agent.

Q: Can my LlamaIndex agent filter which tools from this Nationalize MCP it can use?

Yes. The McpToolSpec supports an allowedtools filter. Since this server only has one tool, predictnationality, you can use this to explicitly grant access, which is good practice for security.

Q: Is it possible to analyze past Nationalize predictions with LlamaIndex?

That's the core idea. By indexing the results of every predictnationality call, you build a historical record. You can then write queries to analyze trends, find patterns, or check past results without making new API calls.

Q: How is my data handled when I use the Nationalize MCP server with LlamaIndex?

The only data sent to this server is the name for the predictnationality call. Vinkius processes this in a secure, isolated environment and doesn't keep logs of the names. The resulting nationality data is returned to your LlamaIndex agent, which then manages it according to your indexing strategy.

Turn nationality predictions into a queryable knowledge base. Ground your LlamaIndex RAG apps in real-world name data.

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Connect Nationalize MCP to LlamaIndex

Create your Vinkius account to connect Nationalize 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 Nationalize with LlamaIndex

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Index Nationality Predictions

Don't just call an API; build a knowledge graph. Every time your LlamaIndex agent uses `predict_nationality`, the result—the name, the countries, the probabilities—can be automatically indexed into your vector store. Now you can ask questions about your own data. For instance, 'What were the most common predicted nationalities for last month's signups?' Your agent queries the indexed `predict_nationality` results to give you an answer grounded in facts, not a new API call.

Augment Documents with Name Data

Imagine you're indexing a list of author names from research papers. With LlamaIndex, you can build a RAG pipeline that automatically calls `predict_nationality` for each author. The predicted origin gets embedded as metadata right alongside the author's name and publications. This creates a much richer dataset. Your queries can now be more specific, like 'Find papers on quantum computing written by authors likely from Germany.' The agent combines semantic search on the paper content with a metadata filter on the indexed nationality data.

Build Smarter RAG with this MCP Server

This MCP Server gives your query engine a new trick. When a user asks a question that includes a person's name, your LlamaIndex agent can use `predict_nationality` on the fly. It enriches the query *before* hitting your vector database. For example, a query like 'What are the import regulations for someone named Dupont?' could trigger the agent to first predict 'France', then refine the search to 'French import regulations.' It's about adding context to make your RAG system smarter.

Setup guide

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

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Common questions about Nationalize MCP in LlamaIndex

First, you initialize the `BasicMCPClient` pointing to this server's endpoint. Then, you wrap it with `McpToolSpec` and call `to_tool_list_async()` to get a list of tools, including `predict_nationality`, ready for your agent.

Yes. The `McpToolSpec` supports an `allowed_tools` filter. Since this server only has one tool, `predict_nationality`, you can use this to explicitly grant access, which is good practice for security.

You configure your LlamaIndex pipeline to treat the output of the `predict_nationality` tool as a document or a node. LlamaIndex then handles the embedding and storage in your chosen vector DB. The next time you query, that API result is part of your searchable knowledge base.

That's the core idea. By indexing the results of every `predict_nationality` call, you build a historical record. You can then write queries to analyze trends, find patterns, or check past results without making new API calls.

The only data sent to this server is the name for the `predict_nationality` call. Vinkius processes this in a secure, isolated environment and doesn't keep logs of the names. The resulting nationality data is returned to your LlamaIndex agent, which then manages it according to your indexing strategy.

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