How to Use the Adzuna Alternative MCP in LangChain

Build multi-step labor market reasoning pipelines in LangChain using real-time job vacancy data.

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Connect Adzuna Alternative MCP to LangChain

Create your Vinkius account to connect Adzuna Alternative 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 Adzuna Alternative with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

LangChain ReAct agents for job market analysis

Your ReAct agent pulls live employment listings using `search_jobs` right at the start of the chain. You feed the output directly into a classification prompt to filter roles by experience level. Passing `get_categories` results into the next step lets the agent decide which sector to investigate next. LangSmith traces exactly how many tokens the prompt burned while mapping these sectors.

Chain salary estimates into compensation reports

Calling `get_jobsworth` gives your pipeline a raw salary estimate based on a specific job title. The agent takes that float value and compares it against historical baselines before writing a final summary. You can then trigger `get_histogram` to pull distribution data for the entire role. This creates a data-dense payload that your next LangChain node formats into a markdown table for HR teams.

Route regional vacancy data through MCP Server

The `get_top_companies` tool lets your agent identify which corporations are hiring the most engineers right now. It outputs a clean array that your script iterates over. Adding `get_geodata` to the chain plots where those vacancies actually sit on a map. You build a pipeline that starts with a city name and ends with a sorted list of top employers in that specific zip code.

Setup guide

Set up Adzuna Alternative 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 Adzuna Alternative 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({
    "adzuna-alternative-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 Adzuna Alternative 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 Adzuna. 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 Adzuna Alternative MCP in LangChain

Install the langchain-mcp-adapters package. You configure a MultiServerMCPClient pointing to the Vinkius endpoint and pass the resulting tools to your agent.

Yes, LangSmith handles this automatically. Every call to fetch salary data or job listings shows up as a distinct trace with exact latency and token metrics.

You absolutely can. The tools plug directly into stateful graph nodes, letting you loop through job searches until you find matches meeting specific criteria.

You get structured JSON arrays containing salary buckets and frequency counts. Your agent parses this directly without needing complex regex or string manipulation.

Vinkius runs the server in an isolated V8 sandbox. When your script queries salary estimates or regional vacancy data, the container spins up, executes the API call, and dies immediately. Nothing persists on disk.

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