Connect Momence to ChatGPT: Automate Bookings and Memberships

If you need to connect Momence to ChatGPT to automate studio management, class scheduling, or membership billing, you need a Model Context Protocol (MCP) server. This server acts as the translation layer between ChatGPT's tool calls and the underlying Momence REST API. You can either spend weeks building and maintaining this infrastructure yourself, or use a managed integration platform like Truto to dynamically generate a secure, authenticated MCP server URL.

If your team uses Claude, check out our guide on connecting Momence to Claude, or explore our broader architectural overview on connecting Momence to AI Agents.

Giving a Large Language Model (LLM) read and write access to a specialized studio platform like Momence is a serious engineering challenge. You have to handle API authentication, map complex JSON schemas for specialized fitness industry data models, and deal with strict workflow dependencies - like requiring a pricing pre-flight check before you can execute a checkout transaction. Every time Momence updates an endpoint, your custom connector logic breaks.

This guide breaks down exactly how to use Truto to generate a secure, managed MCP server for Momence, connect it natively to ChatGPT, and execute complex studio workflows using natural language.

The Engineering Reality of the Momence API

A custom MCP server is a self-hosted integration layer that translates an LLM's generic intent into strict, structured REST API requests. While the open MCP standard provides a predictable way for models to discover tools, the reality of implementing it against specialized vendor APIs is painful.

If you decide to build a custom MCP server for Momence, you own the entire API lifecycle. You are not just building CRUD wrappers. Momence has highly specific domain logic that breaks standard API assumptions. Here are the distinct challenges of working with the Momence API:

The Checkout Pre-Flight Maze An LLM wants to simply execute a tool called buy_membership. But Momence's architecture does not allow this. To process a checkout for a member's cart, you cannot just hit a /checkout endpoint with an item ID. You must first call a /compatible-memberships endpoint to verify the item is valid for that member. Then, you must call a /prices endpoint to fetch the exact, dynamically calculated checkout prices. Only then can you pass that verified payload into the final host checkout endpoint. If your MCP server does not expose these endpoints distinctly and force the LLM to chain them, the LLM will hallucinate the checkout payload and the API will reject the request.

Host vs. Member Scoping Momence bifurcates its API into "Host" actions (the studio owner) and "Member" actions (the end customer). There is a list_all_momence_host_sessions endpoint and a list_all_momence_member_self_sessions endpoint. When an AI agent operating on behalf of a studio manager tries to view a member's schedule, it must use the correct scoped endpoints and pass the target memberId. Mixing up authentication scopes or endpoints will result in persistent 403 Forbidden or 404 Not Found errors.

Complex Membership Freezes Scheduling a membership pause is a multi-step operation. Momence requires explicit freezeType, freezeAt, unfreezeType, and unfreezeAt parameters. If a studio manager asks an AI to "pause Sarah's membership for two months starting next week", the MCP server must provide a highly structured JSON Schema that forces the LLM to parse relative dates into exact ISO timestamps, categorize the freeze type, and target the specific bought_membership_id.

The Managed MCP Approach

Instead of forcing your engineering team to build a custom proxy server, handle pagination cursors, write complex JSON schemas by hand, and manage credential storage, you can use Truto.

Truto dynamically generates MCP tools based on the existing documentation and configuration of the Momence integration. The process is entirely documentation-driven. When an integration endpoint has a defined schema and description in Truto, it automatically becomes an available tool on the MCP server.

Each generated MCP server is scoped strictly to a single integrated account (a specific connected instance of Momence). The server URL contains a cryptographic token that securely handles authentication, meaning the URL itself is all the LLM client needs to connect.

Step 1: Generating the Momence MCP Server

You can generate an MCP server for your connected Momence account via the Truto dashboard or programmatically via the API.

Method A: Via the Truto UI

1. Log into your Truto environment and navigate to the integrated account page for your connected Momence instance.
2. Click the MCP Servers tab.
3. Click Create MCP Server.
4. Configure the server. You can name it (e.g., "Momence Production"), select method filters (e.g., restricting it to "read" methods only), and set an expiration date if this is temporary access.
5. Click Save. Copy the generated MCP server URL (it will look like https://api.truto.one/mcp/a1b2c3...). Keep this URL secret.

Method B: Via the API

For teams building automated deployments, you can create the server programmatically by sending a POST request to the Truto API. This validates that the integration has tools available, generates a secure token stored in a distributed key-value store, and returns the endpoint.

curl -X POST https://api.truto.one/integrated-account/<momence_integrated_account_id>/mcp \
  -H "Authorization: Bearer YOUR_TRUTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Momence Studio Assistant",
    "config": {
      "methods": ["read", "write", "custom"]
    }
  }'

The response returns the secure URL:

{
  "id": "mcp-12345",
  "name": "Momence Studio Assistant",
  "config": { "methods": ["read", "write", "custom"] },
  "expires_at": null,
  "url": "https://api.truto.one/mcp/a1b2c3d4e5f67890"
}

Step 2: Connecting the MCP Server to ChatGPT

Once you have the Momence MCP server URL, you must add it to ChatGPT so the model can discover the available Momence tools (see our post on bringing custom connectors to ChatGPT).

Method A: Via the ChatGPT UI (Custom Connectors)

1. Open ChatGPT and navigate to Settings -> Apps -> Advanced settings.
2. Enable Developer mode (MCP support requires this flag).
3. Under MCP servers / Custom connectors, click to add a new server.
4. Name the connection (e.g., "Momence Studio Ops").
5. Paste the Truto MCP URL into the Server URL field and click Save.

ChatGPT will immediately perform the initialization handshake, pull the JSON schemas for the Momence integration, and populate the model's context with the tools.

Method B: Via Manual Config File (SSE Transport)

If you are running an alternative desktop client, custom agentic framework, or testing locally with an MCP inspector, you can use the Server-Sent Events (SSE) transport approach via standard MCP tooling. Your configuration file will look like this:

{
  "mcpServers": {
    "momence-truto": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "--url",
        "https://api.truto.one/mcp/a1b2c3d4e5f67890"
      ]
    }
  }
}

Security and Access Control

Giving an LLM unconstrained access to a studio management platform is dangerous. An unchecked agent could delete member profiles or refund massive transactions. Truto provides four native security controls to restrict what an MCP server can do:

• Method Filtering: Use config.methods to strictly allow only certain operations. Setting this to ["read"] ensures the LLM can only query data (lists, gets) and physically cannot execute creates, updates, or deletes.
• Tag Filtering: Use config.tags to limit tool exposure by domain. If you only want an agent handling class scheduling, you can restrict the server to the ["scheduling"] tag, hiding all financial and membership billing endpoints.
• API Token Authentication: Setting require_api_token_auth: true forces the calling client to provide a valid Truto API token in the Authorization header. This ensures that even if the MCP URL is leaked in logs, unauthorized external actors cannot execute tools.
• Expiration: The expires_at configuration creates a time-to-live for the server. Truto uses a distributed scheduling system to automatically delete the access token from the key-value store when the timestamp is reached, severing the LLM's access.

Hero Tools for Momence

When the MCP server initializes, it exposes Momence's API endpoints as structured JSON-RPC tools. Here are the highest-leverage tools available for ChatGPT to automate studio operations.

List All Momence Host Sessions

This tool retrieves the studio's schedule, filtering classes by date, teacher, and type. The LLM must explicitly map pagination cursors if fetching large timeframes.

Contextual note: The generated schema instructs the LLM to pass back the next_cursor value exactly as it received it, preventing the model from trying to mathematically guess the next page offset.

"What classes do we have scheduled for tomorrow afternoon, and who is teaching them?"

Create a Momence Checkout Price

Before a checkout can occur, the LLM must calculate exact cart pricing. This pre-flight tool takes an array of desired items and returns the dynamically calculated total, including applicable membership discounts.

Contextual note: If an AI agent tries to skip this step and go straight to checkout, the Momence API will reject the transaction.

"Calculate the total price for adding a 10-class pack and a water bottle to Sarah's cart."

Create a Momence Host Checkout

This tool executes the final transaction on behalf of a studio member, capturing funds using saved payment methods or applying existing membership credits.

Contextual note: The LLM must construct a strict array of items and paymentMethods exactly matching the validation rules enforced by the pricing pre-flight tool.

"Process the transaction for Sarah's 10-class pack using her default card on file."

Create a Momence Waitlist Booking

When a session reaches capacity, this tool safely adds a member to the waitlist queue without triggering standard booking deductions.

Contextual note: The LLM requires both the session_id and the target memberId to successfully execute this call.

"The 6 PM Vinyasa class is full. Add John Doe to the waitlist for that session."

Schedule Bought Membership Freezes Bulk Update

This highly specialized tool manages membership pauses. It handles immediate freezes or schedules future holds based on exact date parameters.

Contextual note: The LLM must correctly map natural language like "next Friday" to the ISO datetime string required by the freezeAt property in the JSON schema.

"Sarah is going on vacation. Schedule a freeze on her unlimited membership starting next Monday and automatically unfreeze it on the 15th of next month."

List All Momence Host Members

This tool queries the studio's core customer directory. It returns vital context, including the user's memberId, which is required for almost all downstream booking and billing tools.

Contextual note: The LLM frequently uses this as a discovery step to resolve a human name to a system ID before taking action.

"Find the profile for John Doe and tell me how many total visits he has on record."

For a complete list of all available Momence operations, including schema definitions and request requirements, visit the Momence integration page.

Workflows in Action

When these tools are provided to ChatGPT, the model handles the complex orchestration of data lookups and payload construction. Here are two real-world workflows demonstrating how AI agents navigate Momence's specific domain logic.

Workflow 1: Managing Class Capacity and Waitlists

Front-desk staff often deal with rapid scheduling requests. Instead of clicking through five different dashboard screens, they can delegate the entire flow to ChatGPT.

"John Smith wants to join the 6 PM Vinyasa class tonight. Book him in. If it's full, put him on the waitlist and let me know."

1. Find Member ID: ChatGPT calls list_all_momence_host_members to search for "John Smith" and extracts his memberId.
2. Find Session ID: The agent calls list_all_momence_host_sessions filtering for today's date and the string "Vinyasa" to retrieve the target session_id.
3. Evaluate Capacity: The LLM reads the capacity and bookingCount fields from the session response. Recognizing the class is full, it dynamically alters its execution plan.
4. Execute Waitlist: ChatGPT calls create_a_momence_waitlist_booking, passing the session_id and memberId to secure John's place in the queue.

The user receives a concise summary: "John Smith's profile was found. The 6 PM Vinyasa class is currently at maximum capacity (30/30). I have successfully added John to the waitlist for this session."

sequenceDiagram
    participant User as ChatGPT User
    participant Truto as Truto MCP Server
    participant Momence as Momence API

    User->>Truto: "Book John Smith into 6 PM Vinyasa. Waitlist if full."
    Truto->>Momence: GET /members (Search: John Smith)
    Momence-->>Truto: Returns memberId: 88472
    Truto->>Momence: GET /sessions (Filter: Today, Vinyasa)
    Momence-->>Truto: Returns session_id: 1109, capacity: 30, bookings: 30
    Truto->>Momence: POST /waitlist (session_id: 1109, memberId: 88472)
    Momence-->>Truto: 201 Created (Waitlist confirmed)
    Truto-->>User: "Class is full. John has been waitlisted."

Workflow 2: Orchestrating a Membership Freeze

Handling customer support for membership pauses requires precise chronological tracking and referencing the exact purchased product.

"Emily Davis is injured. Freeze her active unlimited yoga membership starting tomorrow, and set it to unfreeze automatically in exactly 30 days."

1. Identify the Customer: ChatGPT calls list_all_momence_host_members to locate Emily Davis and extract her memberId.
2. Identify the Asset: The agent calls list_all_momence_bought_memberships_actives using the memberId. It scans the response to find the ID corresponding to the "unlimited yoga membership".
3. Calculate Timestamps: The LLM computes the ISO datetime strings for "tomorrow" and "tomorrow + 30 days".
4. Schedule the Freeze: The agent calls momence_member_bought_membership_schedule_freezes_bulk_update, injecting the bought_membership_id, freezeType, freezeAt, unfreezeType, and unfreezeAt parameters.

The user receives confirmation that the highly specific billing logic has been executed without requiring them to calculate prorated dates or navigate complex UI menus.

Handling Rate Limits in Production

When deploying AI agents to interact with third-party systems, rate limiting becomes a critical architectural consideration. LLMs execute tasks rapidly; if an agent is asked to summarize notes for 500 members, it might try to fire hundreds of parallel requests.

It is important to understand that Truto does not retry, throttle, or apply exponential backoff on rate limit errors.

When the Momence API returns an HTTP 429 Too Many Requests error, Truto passes that error directly back to the caller. Truto normalizes the upstream rate limit information into standardized IETF headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset).

The caller - whether that is the native ChatGPT client, an MCP Inspector, or a custom LangGraph framework - is strictly responsible for interpreting those headers, implementing backoff logic, and pausing execution until the ratelimit-reset window has cleared. If your agent framework ignores the 429 response, the tool call will fail, and the LLM may hallucinate a successful execution.

Automate Studio Operations with Truto

Building an integration layer that safely translates AI intent into Momence's specific studio management API is a massive undertaking. Hand-coding multi-step checkout processes, maintaining strict member versus host scoping, and managing token refreshes diverts your engineering team from building core product features.

By leveraging Truto's managed MCP server, you can securely expose Momence to ChatGPT in minutes. The documentation-driven tool generation ensures your schemas are always accurate, while robust security configurations keep your studio data locked down.