How to Use the HrFlow.ai MCP in LlamaIndex

Q: How do I make HrFlow.ai tools available to a LlamaIndex agent?

Instantiate the McpToolSpec with your client. It finds all the tools on the MCP Server and makes them available for the agent to use in queries.

Q: How can I use LlamaIndex and HrFlow.ai to find candidates for a job I haven't posted yet?

Index all your existing candidates with listprofiles. Then write a detailed description of the new role and use it as a semantic query against your index. LlamaIndex will find the best matches from your current pool.

Build a recruiting knowledge base with LlamaIndex, turning live HrFlow.ai data into a queryable RAG engine.

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Connect HrFlow.ai MCP to LlamaIndex

Create your Vinkius account to connect HrFlow.ai 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 HrFlow.ai with LlamaIndex

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Index Your Entire Talent Pool

Stop treating your candidate data like a static list. Use LlamaIndex to periodically run `list_profiles` and `list_jobs`, indexing the results into a vector store. Now your entire talent pool is a living knowledge base. This means you can ask complex questions in natural language, like "Show me software engineers who have worked at startups and also have skills in Kubernetes." LlamaIndex finds the relevant profiles from the index it built from HrFlow.ai data.

RAG-Powered Candidate Insights with LlamaIndex

Don't let your agent hallucinate. A LlamaIndex query engine combines your prompt with real data fetched from the HrFlow.ai index. It uses tools like `unfold_profile` and `ask_profile` to get facts, not guesses. When you ask, "Summarize this candidate's fit for the Senior PM role," the engine retrieves the actual profile and job description data first. The answer is grounded in facts from your MCP server, with sources cited.

Parse and Index Resumes on the Fly

A new resume just arrived. Instead of just storing it, use LlamaIndex to immediately call `parse_profile`. The tool returns structured JSON. LlamaIndex then automatically adds this structured profile to your vector index. Ten seconds after the resume lands in your inbox, that candidate is already discoverable through semantic search queries by your recruiting team.

Setup guide

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

Instantiate the `McpToolSpec` with your client. It finds all the tools on the MCP Server and makes them available for the agent to use in queries.

Yes. That's what it's for. You can build a query engine that pulls data from your indexed HrFlow.ai profiles and your internal docs on hiring rubrics simultaneously.

LlamaIndex is built for RAG. It excels at creating and querying a knowledge base from your HrFlow.ai data, making it ideal for question-answering systems about your talent pool.

Index all your existing candidates with `list_profiles`. Then write a detailed description of the new role and use it as a semantic query against your index. LlamaIndex will find the best matches from your current pool.

The HrFlow.ai MCP server is stateless. When you use LlamaIndex, you are creating a persistent index of candidate profiles and job descriptions in your own vector database. You are responsible for securing that database and managing data retention policies for the PII it contains.

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