How to Use the Namely MCP in LangChain

Chain Namely HRIS data directly into your LangChain agents to automate employee lookups and organizational reporting.

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Connect Namely MCP to LangChain

Create your Vinkius account to connect Namely 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 Namely with LangChain

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Build automated HR workflows in LangChain

Connect your agents to the Namely MCP Server to pull real-time organizational data. Use `list_profiles` to identify team members or `get_profile` to grab specific contact details without leaving your chain. Your agent routes these calls dynamically. It fetches the data it needs based on the current step, ensuring your pipeline handles HR queries as part of a multi-step reasoning process.

Connect Namely to your LangChain reasoning pipelines

Feed the output of `list_teams` or `list_groups` into subsequent logic blocks. Your agent parses these lists to map out reporting structures or verify group membership for automated task assignments. This integration allows for complex decision chains. You define the sequence, and the agent executes the tool calls in the order required to resolve your specific HR request.

Observe Namely tool calls in LangChain

Trace every interaction with the Namely API using standard monitoring tools. You see exactly what inputs were passed to `list_jobs` or `list_reports` and how the agent interpreted the response. Debugging your HR automation becomes a matter of checking the trace. You identify why an agent chose a specific tool, allowing you to refine your prompt or chain logic for better accuracy.

Setup guide

Set up Namely 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 Namely 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({
    "namely-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 Namely 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 Namely. 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 Namely MCP in LangChain

Install the MCP adapters and point your client to the Namely endpoint. Namely data flows directly into your agent's tool set once you register the connection.

Yes. You chain the tools together so that the output from one Namely query informs the next. Namely results become the input for your agent's next logical step.

It does. You can aggregate Namely with other services, letting your LangChain agent pull from multiple sources simultaneously during a single execution.

You filter data by using the tool parameters directly in your agent's call. Simply pass the criteria to `list_profiles` to narrow down the results.

The server uses a single endpoint token for authentication. Your Namely data remains isolated within your session, and you control exactly which tools your agent can access.

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