How to Use the HireVue MCP in LlamaIndex

Index your HireVue candidate data and interview schedules into searchable vector stores with the LlamaIndex MCP client.

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Connect HireVue MCP to LlamaIndex

Create your Vinkius account to connect HireVue 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 HireVue with LlamaIndex

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Grounded RAG for Recruitment

LlamaIndex turns raw API responses into queryable knowledge. When your agent calls `list_positions` or `get_position`, it indexes those job requirements directly into your vector store. You can ask your agent questions about open roles and get answers based on live data. This changes how you interact with applicant tracking. Instead of clicking through dashboards, your agent retrieves a profile via `get_candidate`, embeds the text, and lets you semantically search past applicant histories in seconds.

Connect LlamaIndex to HireVue

Setting this up requires minimal code. You wrap the basic MCP client in a McpToolSpec and pass it to your FunctionAgent. The agent immediately understands how to execute `get_interview` to check statuses. You can also restrict what the agent sees. Use the allowed tools filter to expose only `create_candidate` and `create_interview` if you want a dedicated scheduling bot. This keeps the surface area small and secure.

Turn Interviews into Context

Traditional bots hallucinate interview times. This MCP integration pulls exact timestamps using `get_interview` and feeds them into the context window. Your agent knows exactly when a candidate is scheduled. If a hiring manager wants to cancel, the agent finds the right ID from the index and executes `cancel_interview`. The system then updates the vector store, ensuring your RAG application always reflects the current reality of your hiring pipeline.

Setup guide

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

Install llama-index-tools-mcp. Set up a BasicMCPClient, wrap it with McpToolSpec, and call await mcp_tool_spec.to_tool_list_async() to feed the tools into your agent.

Yes. You can write a script that iterates through your pipeline using get_candidate, embedding each response into your vector store for semantic search later.

Only if it checks. You can instruct your RAG pipeline to verify current statuses via get_interview before answering questions about a specific applicant's timeline.

Yes. Use the allowed tools filter to expose read-only tools like get_position while blocking access to update_candidate and create_interview.

The MCP Server handles sensitive video conferencing links and candidate contact details. We route all traffic through a zero-trust architecture that requires a single endpoint token. The server never stores your indexing queries or the resulting API responses.

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