How to Use the Manatal MCP in LangChain

Q: Can I use multiple Manatal tools in one LangChain sequence?

Absolutely. You can define a ReAct agent that calls listjobs to find a role and immediately follows up with listcandidates to fill it, all in one execution loop.

Build complex recruitment pipelines in LangChain by chaining Manatal tools for automated candidate sourcing and job tracking.

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Connect Manatal MCP to LangChain

Create your Vinkius account to connect Manatal 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 Manatal with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain recruitment data in LangChain

Feed the output of `list_jobs` directly into your reasoning chains to identify active openings. Your agent parses these results to decide which specific role needs more candidates. Then, use `list_candidates` to extract profiles matching your job criteria. This creates a direct pipeline from a job requirement to a list of qualified people without manual intervention.

Automate profile retrieval for LangChain agents

Once your agent identifies a candidate ID, it calls `get_candidate` to pull full contact details. The data flows instantly into your next chain link for drafting outreach messages. Stop wasting time copying data between tabs. Your agent handles the heavy lifting by pulling structured records directly into your prompt context.

Map application statuses with LangChain

Use `list_applications` to track where candidates sit in your hiring funnel. Your agent monitors these states to alert you when a candidate moves to the interview stage. Call `get_application` to see detailed feedback or stage history. This gives your agent the context needed to summarize recruitment velocity for your team.

Setup guide

Set up Manatal 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 Manatal 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({
    "manatal-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 Manatal transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Manatal MCP in LangChain

Install the necessary adapters and initialize the client using your Vinkius endpoint. You then pass the tools from the Manatal MCP server into your agent constructor to enable direct API access.

Yes. Since every tool call returns structured JSON, LangChain treats this data as standard input for your LLM. You can chain these results into vector stores or other logic modules.

Your data stays within the secure connection between your client and the Vinkius sandbox. No recruitment records are stored in the agent's memory unless you explicitly define a persistent session.

The server will return an error code through the MCP interface. You should implement basic retry logic within your chain to handle these temporary rate limit blocks.

Absolutely. You can define a ReAct agent that calls `list_jobs` to find a role and immediately follows up with `list_candidates` to fill it, all in one execution loop.

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