How to Use the AirMatch Pro MCP in LangChain

Q: How do I build a lead qualification chain for AirMatch Pro with LangChain?

Start with listleads. Then, create a chain that iterates through each lead ID, calls getlead for details, and uses an LLM to decide if it meets your criteria before moving on.

Q: Does the AirMatch Pro MCP Server for LangChain support session persistence?

The server itself is stateless. For persistence, use the client.session() method in the LangChain MCP adapter to maintain context across multiple tool calls in your chain.

Build hiring pipelines that run themselves. Connect LangChain to your AirMatch Pro account and automate your recruitment workflow.

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Connect AirMatch Pro MCP to LangChain

Create your Vinkius account to connect AirMatch Pro 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 AirMatch Pro with LangChain

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Perplexity

Automate Your Hiring Funnel

Chain together AirMatch Pro tools to build a complete hiring sequence. Your agent can `list_leads`, loop through them to `get_lead` for details, and then decide whether to `create_proposal` for the best fits. It's all about flow. The output from one tool becomes the input for the next. This isn't just calling an API; it's building a logical process that your LangChain agent executes from start to finish.

Manage Your Sales Pipeline in LangChain

Go beyond just hiring. Use the same chaining logic to manage your client work. Have your agent `list_customers`, check for open `list_jobs`, and then draft new work orders using `create_proposal`. You can build agents that monitor job statuses and automatically update your team. It connects your AirMatch Pro data directly to your automated workflows.

Full Observability for Your MCP Server

Every tool call your LangChain agent makes to the AirMatch Pro MCP Server is traced. You see the exact inputs, outputs, latency, and token count for every step. This makes debugging complex chains simple. If a proposal isn't being created correctly, you can pinpoint the exact `create_proposal` call and see what data the agent sent.

Setup guide

Set up AirMatch Pro 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 AirMatch Pro 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({
    "airmatch-pro-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 AirMatch Pro 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 AirMatch Pro. 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 AirMatch Pro MCP in LangChain

Start with `list_leads`. Then, create a chain that iterates through each lead ID, calls `get_lead` for details, and uses an LLM to decide if it meets your criteria before moving on.

Yes, that's the point. Pull customer data from AirMatch Pro with `list_customers` and then use another LangChain tool to send them a follow-up email or add them to a separate CRM.

The server itself is stateless. For persistence, use the `client.session()` method in the LangChain MCP adapter to maintain context across multiple tool calls in your chain.

Wrap your tool calls in a try/except block within your chain's logic. LangSmith tracing will also show you any failed API calls from the MCP server, making it easy to debug.

Your AirMatch Pro data, like lead and proposal information, is passed directly to your LangChain agent through a secure, ephemeral Vinkius sandbox. We don't store your proposal or customer data; it's processed in-memory for each transaction.

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