How to Use the Qualified.io MCP in LlamaIndex

Q: How do I connect the Qualified.io MCP Server to my LlamaIndex RAG pipeline?

Install llama-index-tools-mcp and initialize the client with your server URL. You can then use McpToolSpec to convert tools like getassessmentresult into query tools that your agent can execute.

Q: Can LlamaIndex search through candidate code submissions from Qualified.io?

Yes, your agent can call getassessmentresultexhibit to pull clean candidate code and test outputs. LlamaIndex then vectorizes this data, allowing you to run semantic searches for specific coding patterns.

Q: How do I build a candidate review assistant with Qualified.io and LlamaIndex?

Set up an agent that pulls candidate answers using getassessmentresult. The agent compares the code against your indexed best practices and logs a review using createassessmentresultreview.

Q: How does the integration protect candidate personal information and test scores?

Candidate names and test scores retrieved via getassessmentresult are processed in memory within secure Vinkius sandboxes. No candidate data is permanently cached on the proxy server, keeping your hiring pipeline compliant.

Index Qualified.io test results into LlamaIndex vector stores to search and query candidate code submissions semantically.

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Setup Qualified.io with LlamaIndex

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Indexing candidate test submissions

The Qualified.io MCP Server allows LlamaIndex to fetch candidate submissions using `get_assessment_result` and index them directly into your vector database. This turns raw code assessments into a searchable knowledge base of candidate performance. Your RAG pipeline can then query this index to find specific coding patterns, common errors, or exceptional solutions. This eliminates the need to manually read hundreds of individual code submissions to find top talent.

Semantic search across coding challenges

This MCP integration lets your LlamaIndex agent search your entire test library by calling `list_challenges` and `get_challenge`. The agent indexes the challenge descriptions, making it easy to find matching tests for specific job roles. When a hiring manager asks for a Python database test, the agent queries the index and retrieves the exact challenge ID. The agent can then call `create_assessment` to build a new test based on those semantic matches.

Automated candidate cohort tracking

Your LlamaIndex agent can track group performance by calling `list_assessment_cohorts` and indexing the cohort data. It matches this with individual results pulled via `list_assessment_results` to spot performance trends. This data is stored in your local index, allowing you to ask natural language questions about pass rates or drop-off speeds. The agent gets real-time data from the API, ensuring your hiring reports are always accurate.

Setup guide

Set up Qualified.io 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 Qualified.io 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 Qualified.io tools.",
)
response = await agent.run("List recent Qualified.io 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 Qualified.io. 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 Qualified.io MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the client with your server URL. You can then use `McpToolSpec` to convert tools like `get_assessment_result` into query tools that your agent can execute.

Yes, your agent can call `get_assessment_result_exhibit` to pull clean candidate code and test outputs. LlamaIndex then vectorizes this data, allowing you to run semantic searches for specific coding patterns.

Set up an agent that pulls candidate answers using `get_assessment_result`. The agent compares the code against your indexed best practices and logs a review using `create_assessment_result_review`.

Your agent can check candidate status in your vector store, identify who needs a retake, and run `schedule_retry_assessment_result` directly. This keeps your local database and your assessment platform in sync.

Candidate names and test scores retrieved via `get_assessment_result` are processed in memory within secure Vinkius sandboxes. No candidate data is permanently cached on the proxy server, keeping your hiring pipeline compliant.

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