How to Use the Lattice MCP in LangChain

Q: Is Lattice data secure when using LangChain?

The connection uses a standard endpoint token. Data from getuser or listfeedback stays within your authenticated session.

Connect Lattice to your LangChain agents for direct access to performance data and goal tracking in your chains.

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Connect Lattice MCP to LangChain

Create your Vinkius account to connect Lattice 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 Lattice with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain performance data into reasoning pipelines

Feed employee records directly into your chains. Use `list_users` to identify team members and `get_goal` to pull specific OKR details for your agent to process. Your agent decides the next step based on real-time data. It might trigger `list_tasks` to check for pending items before moving to the next link in your workflow.

Automate feedback loops with your MCP server

Build logic that responds to HR events. The agent uses `list_feedback` to gather input and `get_feedback` to parse specific entries without manual intervention. This setup keeps your chain focused on data analysis. You get full observability through your standard tracing tools while the agent handles the heavy lifting.

Manage review cycles across your agentic workflow

Query review status using `list_reviews` to keep your agents informed about current company milestones. It fetches the right context from `get_review` to ensure your logic remains accurate. Your chains stay updated without needing manual refreshes. The integration keeps the agent's context window clean by only pulling necessary data points.

Setup guide

Set up Lattice 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 Lattice 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({
    "lattice-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 Lattice 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 Lattice. 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 Lattice MCP in LangChain

Tools from the server return structured JSON. Your LangChain agent consumes this output directly as a string or object for further processing.

Yes. You can aggregate tools from several sources into a single client. The agent will treat them as a unified toolset.

The connection uses a standard endpoint token. Data from `get_user` or `list_feedback` stays within your authenticated session.

Yes. You can pass a subset of the nine available tools to your agent constructor. This limits the agent to only what you define.

You handle retries in your agent logic. The server exposes the tools, but your chain defines the error handling and retry policy.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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