# AI Toolkit



## Catalyst MCP Server



--------------------------------------------------------------------------------
title: "Overview"
description: "An overview of Catalyst MCP Server — what MCP is, how Zoho MCP works, and the types of Catalyst MCP Servers available."
last_updated: "2026-07-30T07:09:57.512Z"
source: "https://docs.catalyst.zoho.com/en/ai-toolkit/catalyst-mcp-server/overview/"
service: "AI Toolkit"
related:
- Zoho MCP Help (https://help.zoho.com/portal/en/kb/mcp)
- Zoho MCP Tool List (https://zoho-mcp-manual-tool-guide.onslate.in/)
- Catalyst MCP Server Example Prompts (/en/ai-toolkit/catalyst-mcp-server/example-prompts/)

--------------------------------------------------------------------------------


# Catalyst MCP Server

### What is MCP?

Model Context Protocol (**MCP**), is an open-source standard that defines how AI models and LLMs can connect, interact, and operate on other systems. Using MCP, you can instruct an AI model with precise context on the operation you require it to carry out in another system.

### What is Zoho MCP?

Zoho MCP, is a service that offers you pre-defined, and on-demand MCP servers to which you can add MCP tools to from Zoho and other supported third party services.

A Zoho MCP Server acts as the communicative bridge between your LLM and a supported service. The MCP tools configured in the server provide your LLM with the context it needs to understand and interact with that service. With this context, your LLM can precisely carry out your prompted actions.

<br />

Learn more:<br />

* Zoho MCP service

* List of Zoho MCP supported services

### Catalyst MCP Server

The Catalyst MCP server's primary objective is to provide your preferred LLM with the required context to interact and perform operations in the Catalyst console, without ever leaving your coding environment.

<br />

The Catalyst MCP server, is essentially, a Zoho MCP server that is configured with all the MCP tools of the Catalyst service.

<br />

Learn more: Complete list of Catalyst's MCP tools.

You can invoke two types of Catalyst MCP servers; **Catalyst Global MCP Server**, **Catalyst On-Demand MCP Servers**.

#### Catalyst Global MCP Server

A Global MCP Server does not load all its configured tools with every invocation, instead, it uses dynamic discovery. When a prompt is provided, the LLM infers the required actions and queries the Global MCP Server directory to identify only the tools needed to complete those actions. Only those tools are loaded and enabled for use.

This eliminates unnecessary hallucinations caused by tool overload. Furthermore, it removes the manual effort of selecting tools from the MCP console. Also, using a Global MCP Server effectively lifts any tool limitation put in place by the LLMs by ensuring only the required tool is active at any given time.

The *Catalyst by Zoho* service is MCP-compatible and has a global MCP server. Meaning by just connecting and authorizing this server, your LLM will be able to access all **100+ MCP tools** of the Catalyst service and perform required actions in the console.

The access URL to use Catalyst Global MCP server is: https<span></span>://catalyst.zohomcp.com/mcp/message

#### Catalyst On-Demand MCP Server

An On-Demand MCP Server is a Zoho MCP Server where you manually configure the exact tools required for your operation using the MCP console. Using an on-demand MCP server is recommended when you are absolutely sure about the list of tools that you would require for your operation.

Learn more: Adding Catalyst MCP tools to an MCP server.

A typical access url structure for an on-demand MCP server is:
https<span></span>://<span></span>mcp-server-name-serverID.zohomcp.com/mcp/APIKEY/message


--------------------------------------------------------------------------------
title: "Example Prompts"
description: "Example prompts and use cases to help you get started with Catalyst MCP Server across different AI tools."
last_updated: "2026-07-30T07:09:57.512Z"
source: "https://docs.catalyst.zoho.com/en/ai-toolkit/catalyst-mcp-server/example-prompts/"
service: "AI Toolkit"
related:
- Zoho MCP Help (https://help.zoho.com/portal/en/kb/mcp)
- Zoho MCP Tool List (https://zoho-mcp-manual-tool-guide.onslate.in/)
- Catalyst MCP Server Overview (/en/ai-toolkit/catalyst-mcp-server/overview/)

--------------------------------------------------------------------------------


# Example Prompts

Once you've connected your Catalyst MCP server, you can test its functionality using the following prompts.

### Workflows using only Catalyst MCP Tools

The following prompts can be handled by your preferred MCP Client, if the connected MCP server is either the Catalyst Global MCP Server, or an On-Demand MCP Server configured with only Catalyst MCP Tools.

#### Build a Subscription-Based SaaS Application


I am building a subscription management platform. Enable authentication for the project and configure roles for Super Admin, Account Owner, and Member. Create API Gateway routes for subscription creation, renewal, cancellation, and usage tracking. Deploy the application as a web app and configure CORS domains for the client portal origin. Set up a job pool to manage concurrent subscription renewal jobs and create an immediate job handler for on-demand plan upgrades. Cache active subscription states in a dedicated segment to reduce authentication overhead on every request. Schedule a cron job to run at midnight on the first of every month to trigger renewal processing and store monthly billing summaries as versioned objects in a Stratus bucket called 'billing-archives'. Generate pre-signed URLs for each billing summary so account owners can securely download their invoices without platform authentication.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Catalyst Component</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="2">Authentication</td>
      <td>Enable_Authentication</td>
      <td>Enables user authentication on the project, securing all subscription endpoints before access is granted</td>
    </tr>
    <tr>
      <td>List_All_Roles</td>
      <td>Retrieves existing roles to verify and configure Super Admin, Account Owner, and Member access levels</td>
    </tr>
    <tr>
      <td rowspan="2">API Gateway</td>
      <td>Configure_API_Gateway_Route</td>
      <td>Creates the subscription creation, renewal, cancellation, and usage tracking API endpoints</td>
    </tr>
    <tr>
      <td>Create_CORS_Domain</td>
      <td>Registers the client portal origin to permit cross-origin API requests from the front-end</td>
    </tr>
    <tr>
      <td>Web Client Hosting</td>
      <td>List_All_Web_Apps</td>
      <td>Retrieves web app configurations to support deploying the client-facing subscription portal</td>
    </tr>
    <tr>
      <td>Job Pool</td>
      <td>Create_Job_Pool</td>
      <td>Creates a pool to manage concurrent subscription renewal job execution</td>
    </tr>
    <tr>
      <td>Jobs</td>
      <td>Create_Immediate_Job</td>
      <td>Triggers on-demand plan upgrade jobs instantly for real-time plan change requests</td>
    </tr>
    <tr>
      <td rowspan="2">Cache</td>
      <td>Create_Segment</td>
      <td>Creates a dedicated cache segment to hold active subscription state data</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Stores individual subscription states in the segment to reduce authentication overhead per request</td>
    </tr>
    <tr>
      <td>Cron</td>
      <td>Create_Cron_Job</td>
      <td>Schedules the monthly midnight renewal processing job to run on the first of every month</td>
    </tr>
    <tr>
      <td rowspan="2">Stratus</td>
      <td>Create_Bucket</td>
      <td>Creates the billing-archives bucket to store versioned monthly billing summaries</td>
    </tr>
    <tr>
      <td>Generate_Signed_URL</td>
      <td>Generates pre-signed URLs for account owners to securely download their invoices without platform authentication</td>
    </tr>
  </tbody>
</table>

#### Build a B2B Sales Intelligence and Prospecting Engine


I am building a B2B sales prospecting platform on Catalyst. Use the Lead Enrichment tool to gather detailed company and contact data for a target list of accounts. Use Similar Companies to identify adjacent prospects in the same industry vertical and Tech Stack Finder to qualify each prospect based on their existing technology infrastructure. Store all enriched and qualified lead records in a datastore table called 'Prospect Pipeline' and configure a bulk write job to import any existing CRM exports into the same table. Set up a serverless circuit to orchestrate the full enrichment and qualification workflow end to end. Configure a job pool to manage concurrent enrichment jobs across large prospect batches and schedule a weekly cron job every Monday at 6:00 AM to refresh the pipeline with newly enriched data. Expose the prospect data via API Gateway routes so the front-end sales dashboard can consume ranked and filtered lead lists in real time. Cache the top 100 ranked prospects in a dedicated segment for instant dashboard load. Store all prospect export files in a Stratus bucket called 'sales-intelligence' and generate pre-signed URLs for the sales team to download their assigned prospect lists securely.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Catalyst Component</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="3">Dataverse</td>
      <td>Lead_Enrichment</td>
      <td>Gathers detailed company and contact data to enrich the target prospect records</td>
    </tr>
    <tr>
      <td>Similar_Companies</td>
      <td>Identifies adjacent prospects in the same industry vertical for pipeline expansion</td>
    </tr>
    <tr>
      <td>Tech_Stack_Finder</td>
      <td>Qualifies prospects based on their existing technology infrastructure</td>
    </tr>
    <tr>
      <td rowspan="2">Datastore</td>
      <td>Create_Table</td>
      <td>Creates the Prospect Pipeline table to store all enriched and qualified lead records</td>
    </tr>
    <tr>
      <td>Insert_Rows</td>
      <td>Inserts enriched lead records into the Prospect Pipeline table</td>
    </tr>
    <tr>
      <td>Jobs</td>
      <td>Create_Bulk_Write_Job</td>
      <td>Imports existing CRM exports into the Prospect Pipeline table via a bulk write operation</td>
    </tr>
    <tr>
      <td>Circuits</td>
      <td>Execute_Circuit</td>
      <td>Orchestrates the end-to-end enrichment and qualification workflow from lead intake to scoring</td>
    </tr>
    <tr>
      <td>Job Pool</td>
      <td>Create_Job_Pool</td>
      <td>Creates a pool to manage concurrent enrichment jobs across large prospect batches</td>
    </tr>
    <tr>
      <td>Cron</td>
      <td>Create_Cron_Job</td>
      <td>Schedules the weekly Monday 6:00 AM refresh job to pull in newly enriched pipeline data</td>
    </tr>
    <tr>
      <td>API Gateway</td>
      <td>Configure_API_Gateway_Route</td>
      <td>Exposes prospect data via API routes for the front-end sales dashboard to consume</td>
    </tr>
    <tr>
      <td rowspan="2">Cache</td>
      <td>Create_Segment</td>
      <td>Creates a dedicated cache segment to store the top-ranked prospect records</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Caches the top 100 ranked prospects for instant dashboard load without datastore queries</td>
    </tr>
    <tr>
      <td rowspan="2">Stratus</td>
      <td>Create_Bucket</td>
      <td>Creates the sales-intelligence bucket to store prospect export files</td>
    </tr>
    <tr>
      <td>Generate_Signed_URL</td>
      <td>Generates pre-signed URLs for the sales team to securely download their assigned prospect lists</td>
    </tr>
  </tbody>
</table>

#### Build a Real-Time Logistics and Delivery Tracking Platform


I am building a logistics platform for tracking deliveries across multiple regions. Create API Gateway routes for order intake, driver assignment, status updates, and delivery confirmation mapped to their respective serverless functions. Execute each function via POST and PATCH methods for order creation and status transitions. Configure a job pool to manage concurrent dispatch jobs and create immediate jobs for urgent on-demand delivery assignments. Cache real-time delivery status data in a dedicated segment so the tracking dashboard can poll status without hitting the backend functions on every request. Set up a cron job to run every hour to reconcile undelivered orders and flag exceptions. Store all delivery confirmation documents and proof-of-delivery images in a Stratus bucket called 'delivery-records' with pre-signed URLs generated for customer access. Configure environment variables for all functions with region-specific API keys and routing service credentials. Retrieve application logs filtered by error level to proactively monitor failed delivery events.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Catalyst Component</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>API Gateway</td>
      <td>Configure_API_Gateway_Route</td>
      <td>Creates endpoints for order intake, driver assignment, status updates, and delivery confirmation</td>
    </tr>
    <tr>
      <td rowspan="3">Serverless Functions</td>
      <td>Execute_Function_Via_POST</td>
      <td>Executes order creation and assignment functions via POST for new delivery submissions</td>
    </tr>
    <tr>
      <td>Execute_Function_Via_PATCH</td>
      <td>Executes status update functions via PATCH for incremental delivery status transitions</td>
    </tr>
    <tr>
      <td>Create_Env_Variables</td>
      <td>Sets region-specific API keys and routing service credentials as function environment variables</td>
    </tr>
    <tr>
      <td>Job Pool</td>
      <td>Create_Job_Pool</td>
      <td>Creates a pool to manage concurrent dispatch jobs across simultaneous delivery assignments</td>
    </tr>
    <tr>
      <td>Jobs</td>
      <td>Create_Immediate_Job</td>
      <td>Triggers urgent on-demand delivery assignment jobs instantly for priority dispatches</td>
    </tr>
    <tr>
      <td rowspan="2">Cache</td>
      <td>Create_Segment</td>
      <td>Creates a dedicated cache segment to hold real-time delivery status data</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Caches per-order delivery status so the tracking dashboard polls without backend round-trips</td>
    </tr>
    <tr>
      <td>Cron</td>
      <td>Create_Cron_Job</td>
      <td>Schedules an hourly job to reconcile undelivered orders and surface delivery exceptions</td>
    </tr>
    <tr>
      <td rowspan="2">Stratus</td>
      <td>Create_Bucket</td>
      <td>Creates the delivery-records bucket to store confirmation documents and proof-of-delivery images</td>
    </tr>
    <tr>
      <td>Generate_Signed_URL</td>
      <td>Generates pre-signed URLs for customers to access delivery confirmation records securely</td>
    </tr>
    <tr>
      <td>Logs</td>
      <td>Get_Logs</td>
      <td>Retrieves logs filtered by error level to proactively detect and monitor failed delivery events</td>
    </tr>
  </tbody>
</table>

#### Build a Catalyst-Powered DevOps Automation Platform


I am building a B2B sales prospecting platform on Catalyst. Use the Lead Enrichment tool to gather detailed company and contact data for a target list of accounts. Use Similar Companies to identify adjacent prospects in the same industry vertical and Tech Stack Finder to qualify each prospect based on their existing technology infrastructure. Store all enriched and qualified lead records in a datastore table called 'Prospect Pipeline' and configure a bulk write job to import any existing CRM exports into the same table. Set up a serverless circuit to orchestrate the full enrichment and qualification workflow end to end. Configure a job pool to manage concurrent enrichment jobs across large prospect batches and schedule a weekly cron job every Monday at 6:00 AM to refresh the pipeline with newly enriched data. Expose the prospect data via API Gateway routes so the front-end sales dashboard can consume ranked and filtered lead lists in real time. Cache the top 100 ranked prospects in a dedicated segment for instant dashboard load. Store all prospect export files in a Stratus bucket called 'sales-intelligence' and generate pre-signed URLs for the sales team to download their assigned prospect lists securely.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Catalyst Component</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="3">Dataverse</td>
      <td>Lead_Enrichment</td>
      <td>Gathers detailed company and contact data to enrich the target prospect records</td>
    </tr>
    <tr>
      <td>Similar_Companies</td>
      <td>Identifies adjacent prospects in the same industry vertical for pipeline expansion</td>
    </tr>
    <tr>
      <td>Tech_Stack_Finder</td>
      <td>Qualifies prospects based on their existing technology infrastructure</td>
    </tr>
    <tr>
      <td rowspan="2">Datastore</td>
      <td>Create_Table</td>
      <td>Creates the Prospect Pipeline table to store all enriched and qualified lead records</td>
    </tr>
    <tr>
      <td>Insert_Rows</td>
      <td>Inserts enriched lead records into the Prospect Pipeline table</td>
    </tr>
    <tr>
      <td>Jobs</td>
      <td>Create_Bulk_Write_Job</td>
      <td>Imports existing CRM exports into the Prospect Pipeline table via a bulk write operation</td>
    </tr>
    <tr>
      <td>Circuits</td>
      <td>Execute_Circuit</td>
      <td>Orchestrates the end-to-end enrichment and qualification workflow from lead intake to scoring</td>
    </tr>
    <tr>
      <td>Job Pool</td>
      <td>Create_Job_Pool</td>
      <td>Creates a pool to manage concurrent enrichment jobs across large prospect batches</td>
    </tr>
    <tr>
      <td>Cron</td>
      <td>Create_Cron_Job</td>
      <td>Schedules the weekly Monday 6:00 AM refresh job to pull in newly enriched pipeline data</td>
    </tr>
    <tr>
      <td>API Gateway</td>
      <td>Configure_API_Gateway_Route</td>
      <td>Exposes prospect data via API routes for the front-end sales dashboard to consume</td>
    </tr>
    <tr>
      <td rowspan="2">Cache</td>
      <td>Create_Segment</td>
      <td>Creates a dedicated cache segment to store the top-ranked prospect records</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Caches the top 100 ranked prospects for instant dashboard load without datastore queries</td>
    </tr>
    <tr>
      <td rowspan="2">Stratus</td>
      <td>Create_Bucket</td>
      <td>Creates the sales-intelligence bucket to store prospect export files</td>
    </tr>
    <tr>
      <td>Generate_Signed_URL</td>
      <td>Generates pre-signed URLs for the sales team to securely download their assigned prospect lists</td>
    </tr>
  </tbody>
</table>

#### Build an AI-Powered Content Publishing Platform


I am building a content publishing platform where writers submit articles, editors review them, and publishers approve and schedule them for release. Enable authentication and configure roles for Writer, Editor, and Publisher with appropriate table permissions and scopes for each role. Create API Gateway routes for content submission, review, approval, and scheduling. Configure serverless circuits to orchestrate the end-to-end editorial workflow from submission through to publication. Set up a cron job to automatically publish approved articles at their scheduled time and another to send digest notifications every Friday at 9:00 AM. Store all media assets including images, videos, and attachments in a Stratus bucket called 'content-assets' with pre-signed upload signatures for direct writer uploads. Cache the most recently published articles in a dedicated segment to accelerate front-end load times. Deploy the platform as a web app and configure CORS domains for the editorial dashboard and the public-facing reader portal separately.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Catalyst Component</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="2">Authentication</td>
      <td>Enable_Authentication</td>
      <td>Enables user authentication to secure the content platform across all editorial roles</td>
    </tr>
    <tr>
      <td>List_All_Roles</td>
      <td>Retrieves existing roles to configure Writer, Editor, and Publisher access levels</td>
    </tr>
    <tr>
      <td rowspan="2">API Gateway</td>
      <td>Configure_API_Gateway_Route</td>
      <td>Creates endpoints for content submission, review, approval, and scheduling</td>
    </tr>
    <tr>
      <td>Create_CORS_Domain</td>
      <td>Registers the editorial dashboard and public reader portal as separate allowed CORS origins</td>
    </tr>
    <tr>
      <td>Circuits</td>
      <td>Execute_Circuit</td>
      <td>Orchestrates the end-to-end editorial workflow from submission through to publication</td>
    </tr>
    <tr>
      <td>Cron</td>
      <td>Create_Cron_Job</td>
      <td>Schedules automated article publication at release times and weekly digest notifications every Friday</td>
    </tr>
    <tr>
      <td rowspan="2">Stratus</td>
      <td>Create_Bucket</td>
      <td>Creates the content-assets bucket to store media files including images, videos, and attachments</td>
    </tr>
    <tr>
      <td>Create_Upload_Signature</td>
      <td>Generates pre-signed upload signatures for writers to upload media assets directly from the browser</td>
    </tr>
    <tr>
      <td rowspan="2">Cache</td>
      <td>Create_Segment</td>
      <td>Creates a dedicated cache segment to hold recently published article data</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Caches the most recently published articles to accelerate front-end load times</td>
    </tr>
    <tr>
      <td>Web Client Hosting</td>
      <td>List_All_Web_Apps</td>
      <td>Retrieves web app configurations to support deploying the content platform</td>
    </tr>
  </tbody>
</table>

#### Build a Multi-Region Event Management Platform


I am building an event management platform that handles registrations, ticketing, and post-event reporting across multiple regions. Create API Gateway routes for event creation, attendee registration, ticket generation, and cancellation mapped to serverless functions executed via GET, POST, PUT, and DELETE methods. Enable authentication and configure roles for Event Organizer, Attendee, and Platform Admin. Set up a job pool to manage high-concurrency ticket processing during peak registration windows and configure immediate jobs for on-demand ticket reissuance. Store all event assets including banners, schedules, and speaker profiles in a Stratus bucket called 'event-assets' and generate pre-signed URLs for secure attendee access to event materials. Cache event capacity and availability data in a dedicated segment to prevent overselling under high load. Schedule a cron job to run one hour before each event to send automated reminders and another post-event to trigger a bulk read job exporting attendee data for reporting. Store all exported reports as versioned objects in a Stratus bucket called 'event-reports'.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Catalyst Component</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>API Gateway</td>
      <td>Configure_API_Gateway_Route</td>
      <td>Creates endpoints for event creation, attendee registration, ticket generation, and cancellation</td>
    </tr>
    <tr>
      <td rowspan="4">Serverless Functions</td>
      <td>Execute_Function_Via_GET</td>
      <td>Executes event and ticket retrieval functions via GET requests</td>
    </tr>
    <tr>
      <td>Execute_Function_Via_POST</td>
      <td>Executes event creation and attendee registration functions via POST requests</td>
    </tr>
    <tr>
      <td>Execute_Function_Via_PUT</td>
      <td>Executes event update functions via PUT requests</td>
    </tr>
    <tr>
      <td>Execute_Function_Via_DELETE</td>
      <td>Executes cancellation and cleanup functions via DELETE requests</td>
    </tr>
    <tr>
      <td rowspan="2">Authentication</td>
      <td>Enable_Authentication</td>
      <td>Enables user authentication to secure event creation, ticketing, and registration operations</td>
    </tr>
    <tr>
      <td>List_All_Roles</td>
      <td>Retrieves and configures roles for Event Organizer, Attendee, and Platform Admin</td>
    </tr>
    <tr>
      <td>Job Pool</td>
      <td>Create_Job_Pool</td>
      <td>Creates a pool to manage high-concurrency ticket processing during peak registration windows</td>
    </tr>
    <tr>
      <td rowspan="2">Jobs</td>
      <td>Create_Immediate_Job</td>
      <td>Triggers on-demand ticket reissuance jobs instantly when requested</td>
    </tr>
    <tr>
      <td>Create_Bulk_Read_Job</td>
      <td>Creates a bulk read job to export attendee data for post-event reporting</td>
    </tr>
    <tr>
      <td rowspan="2">Cache</td>
      <td>Create_Segment</td>
      <td>Creates a dedicated cache segment to store event capacity and availability data</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Caches event capacity state to prevent overselling under high registration load</td>
    </tr>
    <tr>
      <td>Cron</td>
      <td>Create_Cron_Job</td>
      <td>Schedules pre-event automated reminder dispatch and post-event attendee data export trigger</td>
    </tr>
    <tr>
      <td rowspan="2">Stratus</td>
      <td>Create_Bucket</td>
      <td>Creates the event-assets and event-reports buckets for event materials and exported attendee data</td>
    </tr>
    <tr>
      <td>Generate_Signed_URL</td>
      <td>Generates pre-signed URLs for attendees to securely access event materials</td>
    </tr>
  </tbody>
</table>

#### Build a Catalyst AppSail-Powered Microservices Platform


I am building a microservices platform on Catalyst using AppSail to host independent services for authentication, payments, notifications, and analytics. Retrieve all existing AppSail configurations and map each service to its corresponding API Gateway route. Configure environment variables per service deployment with the required runtime credentials and service-to-service communication keys. Set up serverless circuits to orchestrate cross-service workflows such as payment confirmation triggering notification dispatch and analytics event logging. Configure a job pool to manage concurrent background processing jobs across all microservices and schedule cron jobs for each service's maintenance window payments reconciliation at 2:00 AM, analytics aggregation at 3:00 AM, and notification queue flush at 4:00 AM. Cache inter-service authentication tokens in a dedicated segment to eliminate redundant token generation on every service call. Store all service-level audit logs in a Stratus bucket called 'microservices-audit' and configure CORS domains for each service's permitted front-end origin. Enable authentication at the platform level and configure roles scoped to each microservice domain.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Catalyst Component</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="3">AppSail</td>
      <td>List_All_AppSail</td>
      <td>Retrieves all existing AppSail service configurations for mapping to API routes</td>
    </tr>
    <tr>
      <td>Get_AppSail</td>
      <td>Retrieves detailed configuration for each individual AppSail service</td>
    </tr>
    <tr>
      <td>Create_Env_Variables</td>
      <td>Sets runtime credentials and service-to-service communication keys per service deployment</td>
    </tr>
    <tr>
      <td rowspan="2">API Gateway</td>
      <td>Configure_API_Gateway_Route</td>
      <td>Maps each microservice to its corresponding API Gateway route</td>
    </tr>
    <tr>
      <td>Create_CORS_Domain</td>
      <td>Registers each service's permitted front-end origin as a separate CORS domain</td>
    </tr>
    <tr>
      <td>Circuits</td>
      <td>Execute_Circuit</td>
      <td>Orchestrates cross-service workflows such as payment confirmation triggering notifications and analytics logging</td>
    </tr>
    <tr>
      <td>Job Pool</td>
      <td>Create_Job_Pool</td>
      <td>Creates a pool to manage concurrent background processing jobs across all microservices</td>
    </tr>
    <tr>
      <td>Cron</td>
      <td>Create_Cron_Job</td>
      <td>Schedules maintenance window jobs for payments reconciliation, analytics aggregation, and notification queue flush</td>
    </tr>
    <tr>
      <td rowspan="2">Cache</td>
      <td>Create_Segment</td>
      <td>Creates a dedicated cache segment to store inter-service authentication tokens</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Caches authentication tokens to eliminate redundant token generation on every service call</td>
    </tr>
    <tr>
      <td>Stratus</td>
      <td>Create_Bucket</td>
      <td>Creates the microservices-audit bucket to store service-level audit logs</td>
    </tr>
    <tr>
      <td rowspan="2">Authentication</td>
      <td>Enable_Authentication</td>
      <td>Enables platform-level authentication to secure all microservice interactions</td>
    </tr>
    <tr>
      <td>List_All_Roles</td>
      <td>Retrieves and configures roles scoped to each microservice domain</td>
    </tr>
  </tbody>
</table>

#### Build a Healthcare Appointment and Records Management System


I am building a healthcare platform for managing patient appointments, medical records, and physician workflows. Enable authentication for the project and configure roles for Patient, Physician, Nurse, and Admin with granular table permissions and scopes ensuring patients can only access their own records. Create API Gateway routes for appointment booking, cancellation, rescheduling, and medical record retrieval mapped to serverless functions. Execute functions via GET for record retrieval and POST and PATCH for appointment management. Configure a serverless circuit to orchestrate the appointment confirmation workflow including availability checks, booking confirmation, and notification dispatch. Set up a cron job to run every morning at 7:00 AM to generate the day's appointment schedule and another at 10:00 PM to archive completed appointment records. Store all patient documents, lab reports, and prescriptions in a Stratus bucket called 'patient-records' with pre-signed URLs generated per patient for secure time-limited document access. Cache physician availability slots in a dedicated segment to accelerate the booking experience. Configure environment variables for all functions with HIPAA-compliant service credentials and encryption keys.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Catalyst Component</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="2">Authentication</td>
      <td>Enable_Authentication</td>
      <td>Enables user authentication for the healthcare platform, securing all patient and physician data</td>
    </tr>
    <tr>
      <td>List_All_Roles</td>
      <td>Configures Patient, Physician, Nurse, and Admin roles with appropriate access levels</td>
    </tr>
    <tr>
      <td rowspan="2">Datastore</td>
      <td>Update_Table_Permissions</td>
      <td>Sets granular table permissions so patients can only read and write their own records</td>
    </tr>
    <tr>
      <td>Update_Table_Scopes</td>
      <td>Configures table scope to enforce per-user data isolation across patient records</td>
    </tr>
    <tr>
      <td>API Gateway</td>
      <td>Configure_API_Gateway_Route</td>
      <td>Creates endpoints for appointment booking, cancellation, rescheduling, and medical record retrieval</td>
    </tr>
    <tr>
      <td rowspan="4">Serverless Functions</td>
      <td>Execute_Function_Via_GET</td>
      <td>Executes medical record and schedule retrieval functions via GET requests</td>
    </tr>
    <tr>
      <td>Execute_Function_Via_POST</td>
      <td>Executes appointment booking functions via POST requests</td>
    </tr>
    <tr>
      <td>Execute_Function_Via_PATCH</td>
      <td>Executes appointment rescheduling functions via PATCH for partial updates</td>
    </tr>
    <tr>
      <td>Create_Env_Variables</td>
      <td>Configures HIPAA-compliant service credentials and encryption keys as function environment variables</td>
    </tr>
    <tr>
      <td>Circuits</td>
      <td>Execute_Circuit</td>
      <td>Orchestrates the appointment confirmation workflow including availability checks and notification dispatch</td>
    </tr>
    <tr>
      <td>Cron</td>
      <td>Create_Cron_Job</td>
      <td>Schedules the 7:00 AM daily schedule generation job and the 10:00 PM completed appointment archival job</td>
    </tr>
    <tr>
      <td rowspan="2">Stratus</td>
      <td>Create_Bucket</td>
      <td>Creates the patient-records bucket to store documents, lab reports, and prescriptions</td>
    </tr>
    <tr>
      <td>Generate_Signed_URL</td>
      <td>Generates per-patient time-limited pre-signed URLs for secure document access</td>
    </tr>
    <tr>
      <td rowspan="2">Cache</td>
      <td>Create_Segment</td>
      <td>Creates a dedicated cache segment to store physician availability slots</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Caches physician availability slots to accelerate the appointment booking experience</td>
    </tr>
  </tbody>
</table>

#### Build a Catalyst-Powered IoT Data Ingestion and Monitoring Platform


I am building an IoT platform on Catalyst to ingest, process, and monitor sensor data from thousands of connected devices across multiple facilities. Configure API Gateway routes for device registration, data ingestion, threshold alerts, and device status updates mapped to serverless functions executed via POST for ingestion and GET for status retrieval. Set up a job pool to manage high-concurrency data ingestion jobs from simultaneous device streams and configure immediate jobs for on-demand device diagnostics triggered by field engineers. Create a serverless circuit to orchestrate the alert workflow evaluating incoming sensor readings against defined thresholds, triggering alerts for breaches, and logging events. Schedule a cron job to run every 15 minutes to aggregate device telemetry and another at midnight to archive the day's sensor data via a bulk read job. Store all archived telemetry data and device configuration files in a Stratus bucket called 'iot-telemetry' as versioned objects. Cache the latest sensor readings per device in a dedicated segment so the monitoring dashboard reflects near real-time device status without querying the datastore on every poll. Retrieve application logs filtered by error level to detect device connectivity failures and configure environment variables for all ingestion functions with facility-specific device authentication credentials.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Catalyst Component</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>API Gateway</td>
      <td>Configure_API_Gateway_Route</td>
      <td>Creates endpoints for device registration, data ingestion, threshold alerts, and device status updates</td>
    </tr>
    <tr>
      <td rowspan="3">Serverless Functions</td>
      <td>Execute_Function_Via_POST</td>
      <td>Executes data ingestion functions via POST for incoming sensor data streams</td>
    </tr>
    <tr>
      <td>Execute_Function_Via_GET</td>
      <td>Executes device status retrieval functions via GET requests</td>
    </tr>
    <tr>
      <td>Create_Env_Variables</td>
      <td>Configures facility-specific device authentication credentials as function environment variables</td>
    </tr>
    <tr>
      <td>Job Pool</td>
      <td>Create_Job_Pool</td>
      <td>Creates a pool to manage high-concurrency data ingestion jobs from simultaneous device streams</td>
    </tr>
    <tr>
      <td rowspan="2">Jobs</td>
      <td>Create_Immediate_Job</td>
      <td>Triggers on-demand device diagnostic jobs instantly for field engineer requests</td>
    </tr>
    <tr>
      <td>Create_Bulk_Read_Job</td>
      <td>Creates a bulk read job to export the day's sensor data for archival at midnight</td>
    </tr>
    <tr>
      <td>Circuits</td>
      <td>Execute_Circuit</td>
      <td>Orchestrates the alert workflow evaluating sensor readings against thresholds and triggering breach notifications</td>
    </tr>
    <tr>
      <td>Cron</td>
      <td>Create_Cron_Job</td>
      <td>Schedules the 15-minute telemetry aggregation job and the midnight sensor data archival job</td>
    </tr>
    <tr>
      <td>Stratus</td>
      <td>Create_Bucket</td>
      <td>Creates the iot-telemetry bucket to store versioned archived telemetry and device configuration files</td>
    </tr>
    <tr>
      <td rowspan="2">Cache</td>
      <td>Create_Segment</td>
      <td>Creates a dedicated cache segment to store the latest sensor readings per device</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Caches the most recent sensor reading per device for near real-time dashboard display</td>
    </tr>
    <tr>
      <td>Logs</td>
      <td>Get_Logs</td>
      <td>Retrieves logs filtered by error level to detect device connectivity failures</td>
    </tr>
  </tbody>
</table>

### Catalyst MCP Tools in Multi-Service Zoho Workflows

You can execute these prompts using:

* **2 Zoho MCP Servers**: Catalyst Global MCP Server and an On-Demand Zoho MCP Server configured with the required MCP Tools of other Zoho services.

* An On-Demand Zoho MCP Server, configured to contain MCP tools from multiple Zoho Services, including Catalyst by Zoho.

#### Build an Automated Customer Support Intelligence Platform


I am building a customer support intelligence platform that connects helpdesk operations with CRM data and analytics. Pull all open and overdue tickets from the helpdesk and enrich each ticket's associated contact with additional company and lead data using Catalyst Dataverse. Cross-reference the enriched contact records against CRM leads and accounts to identify high-value customers experiencing support issues and escalate their tickets with priority tags. Create a Catalyst serverless circuit to orchestrate the full triage workflow receiving new ticket webhooks, enriching the contact, evaluating priority, and triggering an automatic reassignment to the most suitable agent based on department. Store all enriched ticket and contact data in a Catalyst datastore table called 'Support Intelligence' and schedule a cron job every morning at 7:00 AM to generate a daily support health report. Export the report as a bulk read job, store it in a Stratus bucket called 'support-reports', and push a summary into a Zoho Analytics dashboard with a chart report breaking down ticket volume by priority, department, and resolution time.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Zoho Service</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="6">Catalyst by Zoho</td>
      <td>Create_Table</td>
      <td>Creates the Support Intelligence datastore table to store enriched ticket and contact records</td>
    </tr>
    <tr>
      <td>Insert_Rows</td>
      <td>Inserts enriched ticket and contact data into the Support Intelligence table</td>
    </tr>
    <tr>
      <td>Execute_Circuit</td>
      <td>Orchestrates the full triage workflow from ticket webhook receipt through enrichment, priority evaluation, and agent reassignment</td>
    </tr>
    <tr>
      <td>Create_Cron_Job</td>
      <td>Schedules the daily 7:00 AM job to generate and export the support health report</td>
    </tr>
    <tr>
      <td>Create_Bulk_Read_Job</td>
      <td>Exports the daily report data from the datastore for storage in the support-reports Stratus bucket</td>
    </tr>
    <tr>
      <td>Create_Immediate_Job</td>
      <td>Triggers on-demand triage and enrichment for newly received ticket webhooks outside the scheduled run</td>
    </tr>
    <tr>
      <td rowspan="5">Zoho Desk</td>
      <td>getTickets</td>
      <td>Pulls all open and overdue tickets from the helpdesk for triage and enrichment</td>
    </tr>
    <tr>
      <td>searchTickets</td>
      <td>Searches tickets to filter overdue and high-priority cases requiring escalation</td>
    </tr>
    <tr>
      <td>getContact</td>
      <td>Retrieves the contact details associated with each ticket for Dataverse enrichment</td>
    </tr>
    <tr>
      <td>updateTicket</td>
      <td>Updates ticket priority tags to escalate issues from identified high-value customers</td>
    </tr>
    <tr>
      <td>getAgents</td>
      <td>Retrieves available agents by department for automatic ticket reassignment</td>
    </tr>
    <tr>
      <td rowspan="3">Zoho CRM</td>
      <td>getLeadsRecords</td>
      <td>Cross-references enriched contacts against CRM lead records to identify high-value prospects with active support issues</td>
    </tr>
    <tr>
      <td>getAccountsRecord</td>
      <td>Retrieves CRM account data to flag high-value customers experiencing recurring support problems</td>
    </tr>
    <tr>
      <td>Lead_Enrichment</td>
      <td>Enriches ticket contact records with additional company and lead intelligence from Catalyst Dataverse</td>
    </tr>
    <tr>
      <td rowspan="4">Zoho Analytics</td>
      <td>import_data</td>
      <td>Pushes enriched ticket and contact data into the Zoho Analytics workspace</td>
    </tr>
    <tr>
      <td>query_data</td>
      <td>Queries aggregated support data to compile the daily health report</td>
    </tr>
    <tr>
      <td>create_chart_report</td>
      <td>Creates a chart breaking down ticket volume by priority, department, and resolution time</td>
    </tr>
    <tr>
      <td>create_summary_report</td>
      <td>Creates a summary report for the daily support health dashboard</td>
    </tr>
  </tbody>
</table>

#### Launch a Full Product Lifecycle Management Platform


I am building a product lifecycle management platform that tracks products from initial sales estimate through to delivery and invoicing. When a new sales order is approved in Zoho Books, trigger a Catalyst serverless circuit to initiate the fulfillment workflow — create a delivery challan, assign the responsible team member, and update the project task status in the associated project. Configure API Gateway routes for order status, fulfillment tracking, and invoice generation. Set up a recurring invoice profile for subscription-based product orders and enable auto-bill against the customer's stored payment method. Schedule a cron job every Monday at 8:00 AM to generate a weekly sales summary using a bulk read job and store the export in a Stratus bucket called 'sales-exports'. Push all order, invoice, and delivery data into a Zoho Analytics workspace with a pivot report showing revenue by product category and customer, and a summary report tracking order-to-delivery cycle times. Set up a webhook in Zoho Books to notify the Catalyst circuit whenever an invoice is approved or overdue, triggering automated payment reminder emails to the customer.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Zoho Service</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="5">Catalyst by Zoho</td>
      <td>Execute_Circuit</td>
      <td>Triggers the fulfillment workflow when a Books sales order is approved, orchestrating delivery challan creation, team assignment, and project task updates</td>
    </tr>
    <tr>
      <td>Configure_API_Gateway_Route</td>
      <td>Creates API endpoints for order status, fulfillment tracking, and invoice generation</td>
    </tr>
    <tr>
      <td>Create_Cron_Job</td>
      <td>Schedules the weekly Monday 8:00 AM sales summary generation job</td>
    </tr>
    <tr>
      <td>Create_Bulk_Read_Job</td>
      <td>Generates the weekly sales summary by exporting order data from the datastore for storage in the sales-exports bucket</td>
    </tr>
    <tr>
      <td>Create_Serverless_Function</td>
      <td>Creates the webhook handler function that receives and processes Zoho Books invoice approval and overdue events</td>
    </tr>
    <tr>
      <td rowspan="7">Zoho Books</td>
      <td>create_sales_order</td>
      <td>Creates a sales order when a new product order is initiated in the platform</td>
    </tr>
    <tr>
      <td>create_invoice</td>
      <td>Generates invoices for completed and approved product orders</td>
    </tr>
    <tr>
      <td>create_delivery_challan</td>
      <td>Creates a delivery challan when the Catalyst circuit triggers fulfillment for an approved order</td>
    </tr>
    <tr>
      <td>create_recurring_invoice</td>
      <td>Sets up a recurring invoice profile for subscription-based product orders</td>
    </tr>
    <tr>
      <td>enable_recurring_invoice_autobill</td>
      <td>Enables automatic billing against the customer's stored payment method for recurring subscriptions</td>
    </tr>
    <tr>
      <td>create_webhook</td>
      <td>Sets up a Zoho Books webhook to notify the Catalyst circuit when invoices are approved or become overdue</td>
    </tr>
    <tr>
      <td>bulk_invoice_reminder</td>
      <td>Sends automated payment reminder emails to customers with overdue invoices</td>
    </tr>
    <tr>
      <td rowspan="4">Zoho Analytics</td>
      <td>import_data</td>
      <td>Pushes order, invoice, and delivery data into the Zoho Analytics workspace</td>
    </tr>
    <tr>
      <td>create_pivot_report</td>
      <td>Creates a pivot report showing revenue broken down by product category and customer</td>
    </tr>
    <tr>
      <td>create_summary_report</td>
      <td>Creates a summary report tracking order-to-delivery cycle times</td>
    </tr>
    <tr>
      <td>create_chart_report</td>
      <td>Creates a chart report for visual revenue trend analysis across the product portfolio</td>
    </tr>
  </tbody>
</table>

#### Build a Unified B2B Revenue Operations Platform


I am building a revenue operations platform that unifies sales intelligence, CRM pipeline management, financial reporting, and meeting productivity. Use Catalyst Lead Enrichment and Similar Companies to identify and qualify new prospects and push enriched lead records directly into Zoho CRM as new lead records with assigned owners. Configure a CRM workflow rule that triggers when a lead reaches the qualified stage automatically creating a sales estimate in Zoho Books and scheduling a Zoho Meeting with the assigned sales rep and the prospect. After each meeting, retrieve the participant report and store attendance and engagement data in a Catalyst datastore table called 'Meeting Intelligence'. Configure a Catalyst cron job every Friday at 5:00 PM to generate a weekly pipeline report. Also, open deals from CRM, outstanding estimates from Zoho Books, and meeting engagement data from the datastore — then push the consolidated report into a Zoho Analytics dashboard with a chart report showing pipeline value by stage and a summary report tracking estimate-to-deal conversion rates.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Zoho Service</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="6">Catalyst by Zoho</td>
      <td>Lead_Enrichment</td>
      <td>Enriches prospect records with detailed company and contact intelligence for qualification</td>
    </tr>
    <tr>
      <td>Similar_Companies</td>
      <td>Identifies new prospects similar to existing high-value accounts for pipeline expansion</td>
    </tr>
    <tr>
      <td>Tech_Stack_Finder</td>
      <td>Qualifies prospects based on their existing technology infrastructure before CRM entry</td>
    </tr>
    <tr>
      <td>Create_Cron_Job</td>
      <td>Schedules the weekly Friday 5:00 PM pipeline report generation job</td>
    </tr>
    <tr>
      <td>Create_Table</td>
      <td>Creates the Meeting Intelligence table to store post-meeting attendance and engagement data</td>
    </tr>
    <tr>
      <td>Insert_Rows</td>
      <td>Stores participant report and meeting engagement data in the Meeting Intelligence table</td>
    </tr>
    <tr>
      <td rowspan="4">Zoho CRM</td>
      <td>createLeadsRecords</td>
      <td>Pushes enriched prospect records into Zoho CRM as new lead entries with assigned owners</td>
    </tr>
    <tr>
      <td>postWorkflowRule</td>
      <td>Configures the CRM workflow rule that triggers when a lead reaches the qualified stage to initiate the estimate and meeting flow</td>
    </tr>
    <tr>
      <td>getDealsRecords</td>
      <td>Retrieves open deal data from CRM for the weekly pipeline report consolidation</td>
    </tr>
    <tr>
      <td>updateLeadsRecord</td>
      <td>Updates lead records with qualification status and owner assignments after enrichment</td>
    </tr>
    <tr>
      <td rowspan="3">Zoho Books</td>
      <td>create_estimate</td>
      <td>Creates a sales estimate in Zoho Books when a CRM lead reaches the qualified stage</td>
    </tr>
    <tr>
      <td>email_estimate</td>
      <td>Sends the estimate to the prospect for review immediately after creation</td>
    </tr>
    <tr>
      <td>approve_estimate</td>
      <td>Marks estimates as approved for tracking in the estimate-to-deal conversion pipeline</td>
    </tr>
    <tr>
      <td rowspan="3">Zoho Meeting</td>
      <td>createMeetingSession</td>
      <td>Schedules a Zoho Meeting between the assigned sales rep and the qualified prospect</td>
    </tr>
    <tr>
      <td>getParticipantReport</td>
      <td>Retrieves post-meeting attendance and engagement data for storage in the Meeting Intelligence table</td>
    </tr>
    <tr>
      <td>getMeetingDetails</td>
      <td>Retrieves full meeting metadata to store alongside engagement records in the Meeting Intelligence table</td>
    </tr>
    <tr>
      <td rowspan="4">Zoho Analytics</td>
      <td>import_data</td>
      <td>Pushes consolidated pipeline, estimate, and meeting engagement data into the Analytics workspace</td>
    </tr>
    <tr>
      <td>query_data</td>
      <td>Queries CRM deals, Books estimates, and Meeting Intelligence data for the weekly pipeline report</td>
    </tr>
    <tr>
      <td>create_chart_report</td>
      <td>Creates a pipeline value by stage chart report for the weekly revenue summary</td>
    </tr>
    <tr>
      <td>create_summary_report</td>
      <td>Creates an estimate-to-deal conversion rate summary report for the dashboard</td>
    </tr>
  </tbody>
</table>

#### Build a Multi-Channel Customer Engagement and Retention Platform


I am building a customer engagement platform that monitors customer health across support, CRM, calendar, and analytics touchpoints. Pull all contacts from Zoho Desk that have more than three unresolved tickets in the past 30 days and cross-reference them against CRM accounts to identify at-risk customers. For each identified at-risk account, trigger a Catalyst serverless circuit that creates a CRM task for the account owner, schedules a follow-up meeting via Zoho Calendar, and generates a personalised retention estimate in Zoho Books with a special pricing offer applied from a custom pricebook. Store all at-risk account data and engagement actions in a Catalyst cache segment for fast retrieval by the account management dashboard. Configure a Catalyst cron job every Sunday at 6:00 AM to refresh the at-risk account list and a job pool to manage concurrent engagement workflows across large account volumes. Push all customer health metrics ticket volume, deal stage, meeting history, and estimate status into a Zoho Analytics workspace with a summary report tracking engagement actions per account owner and a chart report visualising at-risk account distribution by industry and revenue band.


With Catalyst MCP server connected, the LLM uses the given prompt to access and implement the following Catalyst components:

<table border="1" cellpadding="8" cellspacing="0">
  <thead>
    <tr>
      <th>Zoho Service</th>
      <th>MCP Tool</th>
      <th>Purpose</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="5">Catalyst by Zoho</td>
      <td>Execute_Circuit</td>
      <td>Orchestrates the engagement workflow per at-risk account, triggering CRM task creation, meeting scheduling, and retention estimate generation</td>
    </tr>
    <tr>
      <td>Create_Cache_Item</td>
      <td>Stores at-risk account data and engagement action states in a cache segment for fast retrieval by the account management dashboard</td>
    </tr>
    <tr>
      <td>Create_Job_Pool</td>
      <td>Manages concurrent engagement workflows across large at-risk account volumes</td>
    </tr>
    <tr>
      <td>Create_Cron_Job</td>
      <td>Schedules the Sunday 6:00 AM refresh of the at-risk account list</td>
    </tr>
    <tr>
      <td>Create_Immediate_Job</td>
      <td>Triggers the engagement workflow immediately for newly identified at-risk accounts outside the scheduled refresh</td>
    </tr>
    <tr>
      <td rowspan="4">Zoho Desk</td>
      <td>getTickets</td>
      <td>Pulls open tickets to identify contacts with more than three unresolved tickets in the past 30 days</td>
    </tr>
    <tr>
      <td>searchTickets</td>
      <td>Searches for unresolved tickets from the past 30 days to build the at-risk contact list</td>
    </tr>
    <tr>
      <td>getContact</td>
      <td>Retrieves contact details for ticket owners to cross-reference against CRM accounts</td>
    </tr>
    <tr>
      <td>getContactStatistics</td>
      <td>Gets ticket statistics per contact to identify those exceeding the three-ticket threshold in the 30-day window</td>
    </tr>
    <tr>
      <td rowspan="3">Zoho CRM</td>
      <td>getAccountsRecord</td>
      <td>Retrieves CRM account records to match against identified at-risk contacts and confirm account value</td>
    </tr>
    <tr>
      <td>updateDealsRecord</td>
      <td>Updates deal records for at-risk accounts to flag escalation status for the account owner</td>
    </tr>
    <tr>
      <td>createWorkflowTasks</td>
      <td>Creates a CRM follow-up task for the account owner for each identified at-risk account</td>
    </tr>
    <tr>
      <td rowspan="3">Zoho Books</td>
      <td>create_pricebook</td>
      <td>Creates the custom pricebook containing special retention pricing offers for at-risk accounts</td>
    </tr>
    <tr>
      <td>create_estimate</td>
      <td>Generates a personalised retention estimate with the special pricing offer for each at-risk account</td>
    </tr>
    <tr>
      <td>apply_pricebook_to_invoice</td>
      <td>Applies the retention pricebook pricing to the generated estimate for the at-risk customer</td>
    </tr>
    <tr>
      <td rowspan="3">Zoho Analytics</td>
      <td>import_data</td>
      <td>Pushes customer health metrics including ticket volume, deal stage, meeting history, and estimate status into the Analytics workspace</td>
    </tr>
    <tr>
      <td>create_summary_report</td>
      <td>Creates a report tracking engagement actions taken per account owner across the at-risk portfolio</td>
    </tr>
    <tr>
      <td>create_chart_report</td>
      <td>Creates a chart visualising at-risk account distribution by industry and revenue band</td>
    </tr>
  </tbody>
</table>

### Connect Catalyst MCP Server



--------------------------------------------------------------------------------
title: "Connect Catalyst On-Demand MCP Server to an MCP Client"
description: "Step-by-step guide to creating, configuring, and connecting a Catalyst On-Demand MCP Server to your preferred MCP client."
last_updated: "2026-07-30T07:09:57.519Z"
source: "https://docs.catalyst.zoho.com/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/connect-on-demand-mcp/"
service: "AI Toolkit"
related:
- Zoho MCP Help (https://help.zoho.com/portal/en/kb/mcp)
- Zoho MCP Tool List (https://zoho-mcp-manual-tool-guide.onslate.in/)
- Connect Catalyst Global MCP Server (/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/global-mcp/)

--------------------------------------------------------------------------------


# Connect Catalyst On-Demand MCP Server to an MCP Client

To connect with an on-demand MCP server to an LLM, you need to first generate an MCP URL, by creating and configuring a Catalyst MCP server.

### Create an MCP Server

To create an MCP server:

1. Go to Zoho MCP console.
2. Click **Create MCP Server**.
3. Provide a name for your MCP server, then click **Create**.



Your MCP server will be created.

### Configure MCP Server with Catalyst's MCP Tools

To configure your Catalyst on-demand MCP server:

1. Click **Add Tools**.
2. Search for **Catalyst by Zoho**.
3. Add your required tools.
4. Click **Add Now**.



The required tools will be added to your Catalyst MCP server.

### Get MCP URL

To get the MCP URL:

1. Click **Connect** present in the side-panel of the MCP console.
2. Copy the MCP Server URL.



Your Catalyst on-demand MCP Server is now ready to be connected with your preferred MCP Client.

### Connect Catalyst On-Demand MCP Server

Use the MCP URL to connect your server to your preferred MCP client. The configuration process may vary depending on the client used.




### Connect Catalyst On-Demand MCP Server to Claude.ai

To connect your Catalyst MCP server to Claude.ai:

1. Navigate to the *Connect* section present in the MCP Console.
2. Copy the MCP URL.
3. Go to http<span>://claude.ai/settings/integrations.
4. Click the **Add** drop-down.
5. Click **Add Custom Connectors**.
6. Provide a name for the MCP server, and paste your MCP URL.

The MCP server is now added to your MCP Client. To complete the connection, you need to authorize the MCP Server connection to Claude.

7. Access your MCP server from the list of *Connectors* and click **Connect**.
8. Follow the authorization process, and your MCP server will be connected and ready to use.

<br />

You can now leverage the artificial intelligence of Claude.ai through the Catalyst On-Demand MCP Server, which equips Claude with the required context it needs to understand and execute your operations on the Catalyst console.



### Connect Catalyst On-Demand MCP Server to ChatGPT

To connect your Catalyst MCP server to ChatGPT:

To connect your Catalyst Global MCP Server to ChatGPT:

1. Navigate to the **Profile > Settings > Apps** section in ChatGPT, then click **Create App**.
2. Provide a name and description for you server.
3. Paste your MCP Server URL.
4. Select **OAuth** as the authentication type.
5. Click **Create**.

You will be directed to the Zoho MCP authorization screen in your local host. 

6. Click **Allow**.

Follow the authorization process, and your MCP server will be connected and ready to use.

<br />

You can now leverage the artificial intelligence of ChatGPT through the Catalyst On-Demand MCP Server, which equips ChatGPT with the context it needs to understand and execute your operations on the Catalyst console.



### Connect Catalyst On-Demand MCP Server to Cursor

Unlike other MCP Clients that require manual steps to add MCP servers, Cursor offers you automatic configuration, in addition to manual configuration.



To connect your MCP server to Cursor using **automatic configuration**:

1. Navigate to the *Connect* section in the MCP console.
2. Select **Cursor** from the list of MCP Clients.
3. Click **Add to Cursor**.

The required configurations to add your MCP server to Cursor will be automatically handled.

However, for Cursor to access your MCP server and the tools configured in it, you need to connect and provide the required authorizations.

<br />



To connect your Catalyst MCP server to Cursor:

1. Navigate to the *Connect* section present in the MCP Console.
2. Copy the MCP URL.
3. Navigate to the **Tools & Integrations** tab in the *Settings* section in Cursor, and click **Add Custom MCP**.
4. You will be directed to the **mcp_config.json** file.
5. Paste the following code snippet in the file and replace **Your-MCP-URL** with the MCP URL you copied:

{
  "mcpServers": {
    "ZohoMCP": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "Your-MCP-URL",
        "--transport",
        "http-only"
      ]
    }
  }
}


Once you click **Save**, you will be directed to the Zoho MCP authorization screen in your local host.

6. Click **Allow**.

Follow the authorization process, and your MCP server will be connected and ready to use.





You can now leverage the artificial intelligence of Cursor's AI models through the Catalyst On-Demand MCP Server, which equips them with the context they need to understand and execute your operations on the Catalyst console.




### Connect Catalyst On-Demand MCP Server to CoPilot

Unlike other MCP Clients that require manual steps to add MCP servers, VS Code offers you automatic configuration in addition to manual configuration.



To connect your MCP server to VS Code using **automatic configuration**:

1. Navigate to the *Connect* section present in the MCP console.
2. Select **VS Code** from the list of MCP Clients.
3. Click **Add to VS Code**.

The required configurations to add your MCP server to VS Code will be automatically handled.

However, for VS Code to access your MCP server and the tools configured in it, you need to connect and provide the required authorizations.

<br />


To manualy connect your Catalyst MCP server to CoPilot:

1. Navigate to the *Connect* section present in the MCP Console.
2. Copy the MCP URL.
3. Ensure GitHub Copilot is installed, active, and set to **Agent** mode.
4. Access the VS Code Command Palette.
    - On Mac: **⇧ + ⌘ + P**
    - On Windows: **Ctrl + Shift + P**
5. Search for "**MCP:Add Server...**".
6. Choose the "**HTTP (HTTP or Server-Sent Events)**" option, then press **Enter**.
7. In the **Server Key** input field, paste the MCP URL you copied, then press **Enter**.
8. Provide a name for your MCP server and press **Enter**.

You will be directed to the Zoho MCP authorization screen in your local host.

9. Click **Allow**.

Follow the authorization process, and your MCP server will be connected and ready to use.





You can now leverage the artificial intelligence of the AI models present in CoPilot through the Catalyst On-Demand MCP Server, which equips them with the context they need to understand and execute your operations on the Catalyst console.




### Connect Catalyst On-Demand MCP Server to Windsurf

To connect your Catalyst MCP server to Windsurf:

1. Navigate to the *Connect* section present in the MCP Console.
2. Copy the MCP URL.
3. Navigate to Windsurf's **Settings**.
4. Search for "mcp" in the Settings search bar, and click **Manage Plugins**.
5. Click **View Raw Config**.
6. You will be directed to the **mcp_config.json** file. Paste the following code snippet in this file and replace **Your-MCP-URL** with the MCP URL you copied:

{
  "mcpServers": {
    "ZohoMCP": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "Your-MCP-URL",
        "--transport",
        "http-only"
      ]
    }
  }
}


Once you click **Save**, you will be directed to the Zoho MCP authorization screen in your local host.

7. Click **Allow**.

Follow the authorization process, and your MCP server will be connected and ready to use.

You can now leverage the artificial intelligence of Windsurf's AI models through the Catalyst On-Demand MCP Server, which equips them with the context they need to understand and execute your operations on the Catalyst console.




### Connect Catalyst On-Demand MCP Server to a Custom MCP Client

To connect your Catalyst MCP server to a custom MCP client, ensure:

* It is compatible to accept remote MCP servers.
* It supports connection through **HTTP** or **Streamable Transport**.

Once you confirm the fulfillment of the above requirements:

1. Navigate to the *Connect* section present in the MCP Console.
2. Copy the MCP URL.
3. Ensure **Agent mode** or its equivalent mode is enabled in your tool.
4. Create a **mcp_config.json** file or search and find the appropriate file in your AI tool.
5. Paste the following code snippet in the file and replace **Your-MCP-URL** with the MCP URL you copied:

{
  "mcpServers": {
    "ZohoMCP": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "Your-MCP-URL",
        "--transport",
        "http-only"
      ]
    }
  }
}


Follow the authorization process, and your MCP server will be connected and ready to use.

If you are facing further difficulties, reach out to [Catalyst Support](mailto:support@zohocatalyst.com).



#### Connect Catalyst Global MCP Server



--------------------------------------------------------------------------------
title: "Claude.ai"
description: "Connect the Catalyst Global MCP Server to Claude.ai using the universal MCP URL."
last_updated: "2026-07-30T07:09:57.520Z"
source: "https://docs.catalyst.zoho.com/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/global-mcp/connect-global-mcp-claude/"
service: "AI Toolkit"
related:
- Zoho MCP Help (https://help.zoho.com/portal/en/kb/mcp)
- Zoho MCP Tool List (https://zoho-mcp-manual-tool-guide.onslate.in/)
- Connect Catalyst On-Demand MCP Server (/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/connect-on-demand-mcp/)

--------------------------------------------------------------------------------


# Connect Catalyst Global MCP Server to Claude.ai

A Catalyst Global MCP server has a universal MCP URL, you can use to connect to your required MCP Client (LLM). No MCP Tool configuration needs to be done in the MCP console.

You only have to connect the following MCP URL to Claude.ai.

https<span></span>://catalyst.zohomcp.com/mcp/message

To connect your Catalyst Global MCP Server to Claude.ai:

1. Copy the Catalyst Global MCP URL [https<span></span>://catalyst.zohomcp.com/mcp/message].
2. Go to http:<span>//claude.ai/settings/integrations.
3. Click the **Add** drop-down.
4. Click **Add Custom Connectors**.
5. Provide a name for the MCP server, and paste the global MCP URL. 

The MCP server is now added to your MCP Client. To complete the connection, you need to authorize the MCP Server connection to Claude.

6. Access your MCP server from the list of *Connectors* and click **Connect**.
7. Follow the authorization process, and your MCP server will be connected and ready to use.



You can now leverage the artificial intelligence of Claude.ai through the Catalyst Global MCP Server, which equips Claude with the context it needs to understand and execute your operations on the Catalyst console.

--------------------------------------------------------------------------------
title: "ChatGPT"
description: "Connect the Catalyst Global MCP Server to ChatGPT using the universal MCP URL."
last_updated: "2026-07-30T07:09:57.520Z"
source: "https://docs.catalyst.zoho.com/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/global-mcp/connect-global-mcp-chatgpt/"
service: "AI Toolkit"
related:
- Zoho MCP Help (https://help.zoho.com/portal/en/kb/mcp)
- Zoho MCP Tool List (https://zoho-mcp-manual-tool-guide.onslate.in/)
- Connect Catalyst On-Demand MCP Server (/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/connect-on-demand-mcp/)

--------------------------------------------------------------------------------


# Connect Catalyst Global MCP Server to ChatGPT

For ChatGPT, Catalyst by Zoho has an official Codex plugin that when used will allow you to directly connect to Catalyst Global MCP Server, and additionally provide you with access to Skill files to power Codex as a Catalyst coding agent.

To install the plugin:

1. Navigate to the **Profile > Settings > Plugins** section in ChatGPT, then click **Browse Plugins**.
2. Search for **Catalyst by Zoho** plugin, and select the plugin. 

You will be directed to the Zoho MCP authorization screen in your local host.

Learn more about the ChatGPT Codex Plugin

3. Click **Allow**.

Follow the authorization process, and your MCP server will be connected and ready to use.

<br />

You can now leverage the artificial intelligence of ChatGPT through this plugin, which equips ChatGPT with the context it needs to understand and execute your operations on the Catalyst console.

--------------------------------------------------------------------------------
title: "Cursor"
description: "Connect the Catalyst Global MCP Server to Cursor using the universal MCP URL and mcp_config.json."
last_updated: "2026-07-30T07:09:57.520Z"
source: "https://docs.catalyst.zoho.com/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/global-mcp/connect-global-mcp-cursor/"
service: "AI Toolkit"
related:
- Zoho MCP Help (https://help.zoho.com/portal/en/kb/mcp)
- Zoho MCP Tool List (https://zoho-mcp-manual-tool-guide.onslate.in/)
- Connect Catalyst On-Demand MCP Server (/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/connect-on-demand-mcp/)

--------------------------------------------------------------------------------


# Connect Catalyst Global MCP Server to Cursor

A Catalyst Global MCP server has a universal MCP URL, you can use to connect to your required MCP Client (LLM). No MCP Tool configuration needs to be done in the MCP console.

You only have to connect the following MCP URL to Cursor.

https<span></span>://catalyst.zohomcp.com/mcp/message

To connect your Catalyst Global MCP Server to Cursor:

1. Navigate to the **Tools & Integrations** tab in the *Settings* section in Cursor, and click **Add Custom MCP**.
2. You will be directed to the **mcp_config.json** file. 
3. Paste the following code snippet in the file:

{
  "mcpServers": {
    "ZohoMCP": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://catalyst.zohomcp.com/mcp/message",
        "--transport",
        "http-only"
      ]
    }
  }
}


Once you click **Save**, you will be directed to the Zoho MCP authorization screen in your local host.

4. Click **Allow**.

Follow the authorization process, and your MCP server will be connected and ready to use.

<br />

You can now leverage the artificial intelligence of Cursor's AI models through the Catalyst Global MCP Server, which equips them with the context it needs to understand and execute your operations on the Catalyst console.

--------------------------------------------------------------------------------
title: "CoPilot"
description: "Connect the Catalyst Global MCP Server to GitHub Copilot in VS Code using the universal MCP URL."
last_updated: "2026-07-30T07:09:57.520Z"
source: "https://docs.catalyst.zoho.com/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/global-mcp/connect-global-mcp-copilot/"
service: "AI Toolkit"
related:
- Zoho MCP Help (https://help.zoho.com/portal/en/kb/mcp)
- Zoho MCP Tool List (https://zoho-mcp-manual-tool-guide.onslate.in/)
- Connect Catalyst On-Demand MCP Server (/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/connect-on-demand-mcp/)

--------------------------------------------------------------------------------

  

# Connect Catalyst Global MCP Server to CoPilot

A Catalyst Global MCP server has a universal MCP URL, you can use to connect to your required MCP Client (LLM). No MCP Tool configuration needs to be done in the MCP console.

You only have to connect the following MCP URL to CoPilot.

https<span></span>://catalyst.zohomcp.com/mcp/message

To connect your Catalyst Global MCP Server to CoPilot:

1. Ensure GitHub Copilot is installed, active, and set to **Agent** mode.
2. Access the VS Code Command Palette. 
    - On Mac: **⇧ + ⌘ + P** 
    - On Windows: **Ctrl + Shift + P**
3. Search for "**MCP:Add Server...**".
4. Choose the "**HTTP (HTTP or Server-Sent Events)**" option, then press **Enter**.
5. In the **Server Key** input field, paste the global MCP Server URL, then press **Enter**.
6. Provide a name for your MCP server and press **Enter**.

You will be directed to the Zoho MCP authorization screen in your local host. 

7. Click **Allow**.

Follow the authorization process, and your MCP server will be connected and ready to use.

<br />

You can now leverage the artificial intelligence of the AI models present in CoPilot through the Catalyst Global MCP Server, which equips them with the context they need to understand and execute your operations on the Catalyst console.

--------------------------------------------------------------------------------
title: "Windsurf"
description: "Connect the Catalyst Global MCP Server to Windsurf using the universal MCP URL and mcp_config.json."
last_updated: "2026-07-30T07:09:57.520Z"
source: "https://docs.catalyst.zoho.com/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/global-mcp/connect-global-mcp-windsurf/"
service: "AI Toolkit"
related:
- Zoho MCP Help (https://help.zoho.com/portal/en/kb/mcp)
- Zoho MCP Tool List (https://zoho-mcp-manual-tool-guide.onslate.in/)
- Connect Catalyst On-Demand MCP Server (/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/connect-on-demand-mcp/)

--------------------------------------------------------------------------------

  

# Connect Catalyst Global MCP Server to Windsurf

A Catalyst Global MCP server has a universal MCP URL, you can use to connect to your required MCP Client (LLM). No MCP Tool configuration needs to be done in the MCP console.

You only have to connect the following MCP URL to Windsurf.

https<span></span>://catalyst.zohomcp.com/mcp/message

To connect your Catalyst Global MCP Server to Windsurf:

1. Navigate to Windsurf's **Settings**.
2. Search for "mcp" in the Settings search bar, and click **Manage Plugins**.
3. Click **View Raw Config**.
4. You will be directed to the **mcp_config.json** file. Copy and paste the following code snippet in this file and provide the global MCP URL as the value for the mcp-remote json key.

{
  "mcpServers": {
    "ZohoMCP": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://catalyst.zohomcp.com/mcp/message",
        "--transport",
        "http-only"
      ]
    }
  }
}


Once you click **Save**, you will be directed to the Zoho MCP authorization screen in your local host.

5. Click **Allow**.

Follow the authorization process, and your MCP server will be connected and ready to use.

<insert-gif>

You can now leverage the artificial intelligence of Windsurf's AI models through the Catalyst Global MCP Server, which equips them with the context it needs to understand and execute your operations on the Catalyst console.

--------------------------------------------------------------------------------
title: "Custom MCP Client"
description: "Connect the Catalyst Global MCP Server to any MCP-compatible AI client using the universal MCP URL."
last_updated: "2026-07-30T07:09:57.521Z"
source: "https://docs.catalyst.zoho.com/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/global-mcp/connect-global-mcp-custom/"
service: "AI Toolkit"
related:
- Zoho MCP Help (https://help.zoho.com/portal/en/kb/mcp)
- Zoho MCP Tool List (https://zoho-mcp-manual-tool-guide.onslate.in/)
- Connect Catalyst On-Demand MCP Server (/en/ai-toolkit/catalyst-mcp-server/connect-catalyst-mcp/connect-on-demand-mcp/)

--------------------------------------------------------------------------------

  

# Connect Catalyst Global MCP Server with any MCP Client

A Catalyst Global MCP server has a universal MCP URL, you can use to connect to your required MCP Client (LLM). No MCP Tool configuration needs to be done in the MCP console.

You only have to connect the following MCP URL to your requried MCP Client.

https<span></span>://catalyst.zohomcp.com/mcp/message

If you have a requirement to connect the Catalyst Global MCP server to a custom AI Model, LLM, AI-compatible IDE, then you need to ensure:

* It is compatible to accept remote MCP servers.
* It supports connection through **HTTP** or **Streamable Transport**

Once you confirm the fulfillment of the above requirements, then:

1. Ensure **Agent mode** or its equivalent mode is enabled in your tool.
2. Create a **mcp_config.json** file or search and find the appropriate file in your AI tool.
3. Paste the following code snippet in the file.

{
  "mcpServers": {
    "ZohoMCP": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://catalyst.zohomcp.com/mcp/message",
        "--transport",
        "http-only"
      ]
    }
  }
}


Follow the authorization process, and your MCP server will be connected and ready to use.

If you are facing further difficulties, reach out to [Catalyst Support](mailto:support@zohocatalyst.com).