How to Use the 15Five MCP in LangChain

Q: How do I connect the 15Five MCP Server to my LangChain agent?

Install the langchain-mcp-adapters package and run the multi-server client. You just point the client to the Vinkius HTTP endpoint, call gettools(), and pass those tools directly to your agent runner.

Q: Can LangChain analyze historical 15Five check-ins across different departments?

Yes, your agent can chain listdepartments and listcheckins together. This allows the model to map specific sentiment trends to their respective teams automatically.

Q: How does 15Five handle user lookups in LangChain?

The model uses listusers to find the unique identifier for any employee. Once it has the ID, it can query check-ins or objectives for that specific person.

Q: Can I send a High Five automatically using this integration?

You can trigger sendhighfive within any LangChain run. This MCP tool needs the recipient's email and a text message to execute the call.

Get 15Five performance data and send peer recognition directly within your LangChain agent chains.

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Connect 15Five MCP to LangChain

Create your Vinkius account to connect 15Five 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 15Five with LangChain

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Claude

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Automate Peer Recognition with LangChain

The `send_high_five` tool lets your agent send public appreciation to any employee using just their email address and a custom message. You can wire this tool into a LangChain chain that watches for completed milestones in other tools and automatically fires off recognition. By combining this with `list_users`, the agent finds the exact email and user ID first, ensuring the praise goes to the right person. This setup turns raw work updates into immediate, public company-wide feedback.

Track Team Sentiment in Multi-Step Chains

The `list_checkins` tool pulls down weekly employee submissions and sentiment scores for analysis. LangChain agents can read these updates, flag team members who might need support, and route the notes to managers. Using `list_departments` alongside this tool lets your agent group sentiment data by specific team structures. You get a clear view of how different units are doing without manually digging through individual profiles.

Align OKRs Automatically via MCP Server Tools

The `list_objectives` tool exposes all active company and individual OKRs to your LangChain runtimes. Your agent can pull these goals to check if current tasks align with company priorities. This MCP Server integration gives your autonomous agents the context they need to make smart decisions. It ensures every action matches documented team goals.

Setup guide

Set up 15Five 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 15Five 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({
    "15five-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 15Five 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 15Five. 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 15Five MCP in LangChain

Install the `langchain-mcp-adapters` package and run the multi-server client. You just point the client to the Vinkius HTTP endpoint, call `get_tools()`, and pass those tools directly to your agent runner.

Yes, your agent can chain `list_departments` and `list_checkins` together. This allows the model to map specific sentiment trends to their respective teams automatically.

The model uses `list_users` to find the unique identifier for any employee. Once it has the ID, it can query check-ins or objectives for that specific person.

You can trigger `send_high_five` within any LangChain run. This MCP tool needs the recipient's email and a text message to execute the call.

Your performance metrics, check-ins, and OKRs remain protected inside the Vinkius sandboxed environment. V8 isolates execute all tool calls, ensuring that sensitive HR data is never exposed to external logs or public training sets.

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