How to Use the Momence MCP in LangChain

Q: Can LangChain run multi-step Momence workflows?

Yes. A LangChain agent can use listsessions to find a class, check its capacity with listbookings, and then run getmember to confirm registration details in a single run.

Q: How does the Momence MCP Server handle multi-studio setups in LangChain?

The server uses listlocations to retrieve all your physical locations. Your LangChain agent can then filter sessions and bookings for each specific site.

Connect your LangChain agents directly to Momence to automate studio bookings, manage class schedules, and check member profiles.

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Connect Momence MCP to LangChain

Create your Vinkius account to connect Momence 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 Momence with LangChain

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Chain Momence bookings with LangChain agents

Your agent uses `list_sessions` to pull active class schedules and then feeds that directly into `list_bookings` to check real-time attendance. This links multiple API calls together in a single execution chain, meaning the output of a schedule lookup immediately informs booking decisions. If a class is full, the agent automatically triggers `get_member` to find waitlisted clients or looks up alternative times using `list_session_types`. You get full observability through LangSmith, showing you the exact latency and token cost of every single tool call.

Smart teacher scheduling via MCP Server

The MCP Server exposes `list_teachers` alongside `list_sessions` to help your agent resolve scheduling conflicts. Your chain can run a check across all studio locations with `list_locations` to find where instructors are assigned on any given day. Instead of manual lookups, the agent runs a ReAct loop to verify instructor availability against class demands. This automated workflow runs in the background, outputting a clean schedule conflict report directly to your team.

Deep member lookups in conversational chains

The `search_members` tool lets your agent find any client in your database to check their active memberships and class history. It uses this data to instantly verify if a client has active credits before processing a new class registration. By chaining this with `list_memberships`, the agent can flag expiring plans during a chat. You don't have to write custom integration code; LangChain handles the tool execution sequence while keeping track of the user's conversational state.

Setup guide

Set up Momence 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 Momence 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({
    "momence-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 Momence 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 Momence. 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 Momence MCP in LangChain

Install the MCP adapters package and initialize the MultiServerMCPClient with the Vinkius endpoint URL. Pass the tools retrieved from `client.get_tools()` directly into your agent constructor.

Yes. A LangChain agent can use `list_sessions` to find a class, check its capacity with `list_bookings`, and then run `get_member` to confirm registration details in a single run.

The server uses `list_locations` to retrieve all your physical locations. Your LangChain agent can then filter sessions and bookings for each specific site.

LangSmith traces every step of the tool execution, letting you see exactly how your agent queries `list_products` or `list_teachers`. You can monitor latency and debug failed tool calls instantly.

All sensitive member profiles and contact details accessed via `get_member` are processed in an ephemeral V8 Isolate sandbox. Vinkius does not store your studio's client database or API credentials on disk.

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