How to Use the ProUni Eligibility Calculator MCP in LlamaIndex

Index ProUni Eligibility Calculator results into your knowledge base with LlamaIndex.

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Connect ProUni Eligibility Calculator MCP to LlamaIndex

Create your Vinkius account to connect ProUni Eligibility Calculator to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup ProUni Eligibility Calculator with LlamaIndex

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ProUni Eligibility Calculator for LlamaIndex

The tool output becomes a searchable piece of data in your vector store. When you run the calculator, every result—from `calculate_per_capita_income` to the final course list—is indexed. You can later query past sessions or configurations and get answers grounded in that actual API data. This capability means your knowledge base grows with every calculation, making it a powerful source of truth for students and advisors.

RAG Applications using the MCP Server

You build Retrieval-Augmented Generation (RAG) apps where live data meets documents. The output from `determine_eligibility_tier` can be combined with university handbooks or policy guides in a single, unified index. The result is an answer that cites the specific API calculation and combines it with external documentation, eliminating hallucinations.

Structured Data Indexing via LlamaIndex

The MCP ensures structured data access. After running `identify_eligible_courses`, you don't just get a list; you get a record that can be indexed and filtered by criteria like public school status or scholarship tier. This makes querying complex educational rules much simpler than searching through PDFs.

Setup guide

Set up ProUni Eligibility Calculator 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 ProUni Eligibility Calculator 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 ProUni Eligibility Calculator tools.",
)
response = await agent.run("List recent ProUni Eligibility Calculator 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 ProUni Eligibility Calculator. 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 ProUni Eligibility Calculator MCP in LlamaIndex

You index the output of this MCP into your vector store. Instead of just asking a question, you query the stored data to find out, for instance, all eligible courses that were calculated in a previous session.

Yes. You combine live API results with your documents. For example, you can query: 'Based on the income data stored for this client, what are their options at universities in Boston?'

The primary benefit is grounding your AI responses. You get answers sourced from the exact calculations—like per capita income—and can cite that source, rather than relying on general knowledge.

It does. You can run a chain of tools (income calculation -> tier determination -> location query) and index the final, comprehensive result in one go for later retrieval.

This MCP handles financial metrics: gross family income and household size. It also processes standardized test scores (ENEM scores).

Use it with your favorite AI tools

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