Can I move a candidate to a new stage using the agent?

Yes! Use the `move_candidate` tool with the Position ID, Candidate ID, and the target Stage ID. Your agent will update the pipeline status in Breezy HR instantly.

How do I list all the candidates for a specific job?

Simply ask the agent to `list_candidates` and provide the Position ID. It will retrieve the current applicants and their primary details.

Does the integration allow creating a new job position?

Yes. Use the `create_position` action and provide the name, description, and job type. The position will be created in your account as a draft or published based on your settings.

Breezy HR MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Breezy HR
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Breezy HR as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Breezy HR. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Breezy HR?"
    )
    print(response)

asyncio.run(main())

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About Breezy HR MCP Server

Connect your Breezy HR account to any AI agent and orchestrate your hiring and candidate management workflows through natural conversation.

LlamaIndex agents combine Breezy HR 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.

What you can do

Position Oversight — List and retrieve detailed metadata for all your active and draft job positions.
Candidate Management — Create new candidate profiles, move them through your pipeline, and retrieve full applicant histories.
Pipeline Coordination — List and monitor stages for specific positions to ensure a smooth hiring flow.
Administrative Access — Retrieve company information and task templates straight from your workspace.

The Breezy HR 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 Breezy HR to LlamaIndex via MCP

Follow these steps to integrate the Breezy HR 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 Breezy HR

Why Use LlamaIndex with the Breezy HR MCP Server

LlamaIndex provides unique advantages when paired with Breezy HR through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Breezy HR tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Breezy HR tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Breezy HR, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Breezy HR tools were called, what data was returned, and how it influenced the final answer

Breezy HR + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Breezy HR MCP Server delivers measurable value.

Hybrid search: combine Breezy HR real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Breezy HR 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 Breezy HR for fresh data

Analytical workflows: chain Breezy HR queries with LlamaIndex's data connectors to build multi-source analytical reports

Breezy HR MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Breezy HR to LlamaIndex via MCP:

create_candidate

Add a candidate to a position

create_position

Create a new job position

get_candidate

Get specific candidate details

get_company

Get details of the authenticated company

get_position

Get details of a specific position

list_candidates

List candidates for a specific position

list_positions

List all job positions

list_stages

List pipeline stages for a position

list_task_templates

List available task templates

move_candidate

Move a candidate to a different pipeline stage

Example Prompts for Breezy HR in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Breezy HR immediately.

"List all active job positions in Breezy HR."

"Show the candidates for the 'Senior Developer' role."

"Move candidate cand_123 to the 'Interview' stage."

Troubleshooting Breezy HR MCP Server with LlamaIndex

Common issues when connecting Breezy HR to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Breezy HR + LlamaIndex FAQ

Common questions about integrating Breezy HR MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Breezy HR tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.