How to Use the College Scorecard API MCP in LlamaIndex

Q: Does this MCP server support filtering in LlamaIndex?

You can use the allowedtools filter to restrict which API operations your indexer can trigger. It keeps your agent scope tight and controlled.

Index College Scorecard API results into your LlamaIndex knowledge base for grounded, verifiable answers.

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Connect College Scorecard API MCP to LlamaIndex

Create your Vinkius account to connect College Scorecard API 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 College Scorecard API with LlamaIndex

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Ground your LlamaIndex RAG with school data

Run `search_colleges` and index the results directly into your vector store. Your model stops guessing and starts citing real API data. This creates a searchable knowledge base of university information. You get accurate answers because the context is grounded in live API responses.

Index academic programs using LlamaIndex

Pull data from `get_fields_of_study` and map it into your existing index. Your agent queries this local store to find program details without hitting the API every time. It keeps your responses fast and reliable. The index acts as a buffer between your raw data and the final user response.

Map geographic data in LlamaIndex

Use `list_colleges_by_state` to populate your index with regional school lists. Your agent performs semantic searches across this data to answer complex location-based questions. This MCP server integrates with your tool spec to fetch data on demand. It bridges the gap between static documents and dynamic school statistics.

Setup guide

Set up College Scorecard API 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 College Scorecard API 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 College Scorecard API tools.",
)
response = await agent.run("List recent College Scorecard API 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 College Scorecard. 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 College Scorecard API MCP in LlamaIndex

Use the MCP tool spec to wrap the client. Once connected, your agent can call tools and index the resulting payloads automatically.

Yes, the tool output becomes a part of your searchable index. It allows your model to reference past school data during multi-turn conversations.

The API data provides a grounded source of truth. By indexing the tool output, you force the model to rely on actual figures rather than training data.

The server operates over an encrypted transport. Only the public school metrics you request are processed, ensuring no sensitive information leaves your control.

You can use the allowed_tools filter to restrict which API operations your indexer can trigger. It keeps your agent scope tight and controlled.

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