How to Use the Breezy HR MCP in LlamaIndex

Index your recruitment data into a searchable knowledge base using Breezy HR and LlamaIndex.

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Connect Breezy HR MCP to LlamaIndex

Create your Vinkius account to connect Breezy HR 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 Breezy HR with LlamaIndex

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RAG-powered hiring with LlamaIndex

Turn your recruitment history into a searchable index. By running `list_candidates` and `get_candidate`, your agent populates a vector store with real applicant data for better recall. Your RAG application stays grounded in facts rather than guessing. Queries about past applicants or pipeline history return precise answers sourced directly from your Breezy HR account.

Searchable job positions in LlamaIndex

Never lose track of a job opening again. Your agent indexes `list_positions` and `get_position` results, making your entire active hiring list queryable at any time. This creates a unified knowledge base where API data and internal documents live side-by-side. You get answers based on current, live recruitment data instead of stale reports.

Semantic search for your MCP Server data

Connect your MCP Server to your indexer to keep your data current. As candidates move through stages, the indexed results update, ensuring your agent always has the latest view of your pipeline. It removes the need for manual data syncing. Your agent simply queries the index to find the right candidate for a specific stage.

Setup guide

Set up Breezy HR 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 Breezy HR 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 Breezy HR tools.",
)
response = await agent.run("List recent Breezy HR 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 Breezy HR. 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.

Why Choose Vinkius

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

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place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Breezy HR MCP in LlamaIndex

You wrap the tools in a tool spec and pass them to your agent. This allows the framework to index the output and perform semantic searches against your recruitment records.

No, because the framework grounds its responses in the results returned by the tools. Your agent only answers based on the actual data pulled from your account.

Certainly, you can index all your job positions and search for them by title or status. It makes finding the right requisition a matter of asking your agent a simple question.

The system uses ephemeral memory to process candidate details. Once your agent receives the output, the data resides only within your local indexing pipeline, keeping your sensitive information out of the server's persistent storage.

No, the server uses a single endpoint token for authentication. Your credentials are never stored or exposed to the agent client.

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