How to Use the Breezy HR MCP in LangChain

Q: Can LangChain handle complex Breezy HR candidate moves?

Yes, you can define a chain that uses getcandidate to check status and then movecandidate to update the stage. Your agent determines the logic based on the candidate's current profile.

Chain your recruitment data into custom logic using the Breezy HR MCP Server and LangChain.

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Connect Breezy HR MCP to LangChain

Create your Vinkius account to connect Breezy HR to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate candidate pipelines in LangChain

Pipe Breezy HR data directly into your chains. You can trigger `create_candidate` or `move_candidate` based on external events, letting your agent handle the heavy lifting of pipeline management. Your reasoning loops get smarter when you feed them live data. Use `list_candidates` to feed fresh context into your LangSmith traces, ensuring every decision is backed by the latest applicant status.

Dynamic hiring workflows for LangChain

Stop jumping between tabs to manage job openings. Your agent uses `create_position` and `get_position` to handle the backend work, freeing you to focus on the actual interviews. Construct complex agentic sequences where the output of `list_stages` dictates the next step in your custom hiring flow. It turns your agent into a functional member of the recruiting team.

Instant task management via MCP Server

Need to keep your team on track? Use `list_task_templates` to pull active requirements into your chain, ensuring you never miss a deadline or follow-up item. This MCP Server acts as a bridge between your agent's logic and your actual hiring records. It keeps your data flowing without manual input or constant context switching.

Setup guide

Set up Breezy HR MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Breezy HR tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "breezy-hr-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Breezy HR transactions"
    })
    print(result["messages"][-1].content)

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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.

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Common questions about Breezy HR MCP in LangChain

Install the necessary adapters and initialize the server via the provided endpoint. From there, you map the tools into your agent's available functions to start executing recruitment tasks.

Yes, you can define a chain that uses `get_candidate` to check status and then `move_candidate` to update the stage. Your agent determines the logic based on the candidate's current profile.

It fits right into your existing architecture as a standard tool set. Your agent treats these functions as executable steps within any multi-step reasoning pipeline.

Your candidate records stay within the encrypted transit tunnel between the Vinkius sandbox and your agent. No PII is stored or logged by the server itself during execution.

The server uses a single endpoint token to authenticate your requests. This keeps your API keys isolated from your agent's code while maintaining a secure connection to your account.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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