HRBlade MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Create Interview, Get Candidate Details, List Candidates, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add HRBlade as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The HRBlade app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to HRBlade. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in HRBlade?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About HRBlade MCP Server
Connect your HRBlade account to any AI agent and manage asynchronous video interviews through natural conversation.
LlamaIndex agents combine HRBlade tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Interview Templates — List all interview templates and create new ones with custom questions, time limits, and instructions
- Candidate Invitations — Send video interview invitations to candidates by email with personalized details
- Candidate Management — Browse all candidates in your pipeline and inspect individual profiles with status
- Response Review — List all submitted video responses to review candidate answers and evaluate performance
The HRBlade MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 6 HRBlade tools available for LlamaIndex
When LlamaIndex connects to HRBlade through Vinkius, your AI agent gets direct access to every tool listed below — spanning video-interviewing, candidate-screening, hiring-pipeline, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Pass data as a JSON string. Create a new interview template
Get specific candidate details
List all recruiting candidates
List all candidate video responses
List all interview templates
Pass data as a JSON string. Send an invitation to a candidate
Connect HRBlade to LlamaIndex via MCP
Follow these steps to wire HRBlade into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the HRBlade MCP Server
LlamaIndex provides unique advantages when paired with HRBlade through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine HRBlade tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain HRBlade tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query HRBlade, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what HRBlade tools were called, what data was returned, and how it influenced the final answer
HRBlade + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the HRBlade MCP Server delivers measurable value.
Hybrid search: combine HRBlade real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query HRBlade to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying HRBlade for fresh data
Analytical workflows: chain HRBlade queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for HRBlade in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with HRBlade immediately.
"Create a 'Senior Developer' interview with 3 technical questions and send it to 2 candidates."
"Show all candidates in my pipeline and the latest video responses."
"List all interview templates and show which ones have pending responses to review."
Troubleshooting HRBlade MCP Server with LlamaIndex
Common issues when connecting HRBlade to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHRBlade + LlamaIndex FAQ
Common questions about integrating HRBlade MCP Server with LlamaIndex.