HrFlow.ai MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add HrFlow.ai 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 MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 HrFlow.ai. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in HrFlow.ai?"
)
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 HrFlow.ai MCP Server
Empower your AI agents with HrFlow.ai's advanced talent acquisition capabilities. This MCP server allows you to parse resumes, search profiles and jobs with semantic filters, score candidates against job descriptions, and ask natural language questions about profiles. Ideal for automating recruitment workflows with AI-driven insights.
LlamaIndex agents combine HrFlow.ai tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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.
The HrFlow.ai MCP Server exposes 10 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.
How to Connect HrFlow.ai to LlamaIndex via MCP
Follow these steps to integrate the HrFlow.ai MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from HrFlow.ai
Why Use LlamaIndex with the HrFlow.ai MCP Server
LlamaIndex provides unique advantages when paired with HrFlow.ai through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine HrFlow.ai tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain HrFlow.ai tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query HrFlow.ai, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what HrFlow.ai tools were called, what data was returned, and how it influenced the final answer
HrFlow.ai + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the HrFlow.ai MCP Server delivers measurable value.
Hybrid search: combine HrFlow.ai real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query HrFlow.ai 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 HrFlow.ai for fresh data
Analytical workflows: chain HrFlow.ai queries with LlamaIndex's data connectors to build multi-source analytical reports
HrFlow.ai MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect HrFlow.ai to LlamaIndex via MCP:
ask_profile
Asks a natural language question about a specific profile
list_boards
Lists job boards
list_jobs
Lists jobs stored in HrFlow boards
list_profiles
Lists candidate profiles stored in HrFlow
list_sources
Lists profile sources
parse_profile
Parses a resume file into a structured profile
score_profiles
Scores candidate profiles against a specific job
search_jobs
Searches for jobs with semantic filters
search_profiles
Searches for profiles with semantic filters
unfold_profile
Analyzes and unfolds the career path of a profile
Example Prompts for HrFlow.ai in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with HrFlow.ai immediately.
"List the last 5 profiles added to my HrFlow source."
"Ask profile key 'abc-123' if they have experience with Kubernetes."
"Find jobs that match candidate 'john-doe-key'."
Troubleshooting HrFlow.ai MCP Server with LlamaIndex
Common issues when connecting HrFlow.ai to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHrFlow.ai + LlamaIndex FAQ
Common questions about integrating HrFlow.ai MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect HrFlow.ai with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect HrFlow.ai to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.