ManyMoney AI MCP server

Beta feature notice

The ManyMoney AI MCP server is in beta. The set of available actions, the endpoint, and authentication options may change.

The ManyMoney AI MCP server lets you drive your Pushwoosh project from the AI assistant you already use. Connect it to a Model Context Protocol (MCP) client such as Claude Desktop, Cursor, or Windsurf, and ManyMoney AI can plan, build, launch, and analyze Pushwoosh work on your behalf from natural-language prompts in your editor or chat.

If you have used ManyMoney AI inside the Pushwoosh Control Panel, the MCP server gives you almost the same assistant outside the Control Panel.

What you can do

Once you connect the MCP server, your AI client can work across the Pushwoosh feature areas you use every day.

Area	What you can do
Knowledge base	Get answers about channels, journeys, integrations, and best practices grounded in the live Pushwoosh documentation instead of digging through the help center mid-task.
Applications and account	Launch a new app for a new market, add Android to an iOS-only project, issue an API token for a partner integration, or grant a teammate the right level of access without context-switching out of your chat.
Audience and segmentation	Build the segment you need on demand, like users who bought in the last 30 days but haven’t opened the app in 7. Define the events you target on, tune frequency capping, and adjust the global control group as traffic ramps.
Content	Draft the push, email, SMS, and LINE presets for a campaign in one go, build rich-media in-app messages with personalized offers, and route email links through your own tracking domain so the brand stays consistent.
Customer Journey	Spin up a Welcome flow, an Abandoned Cart recovery, or a Win-Back series. Pause one that isn’t pulling its weight. Clone a winner and adapt it for a new market, then pull the per-step funnel to see where users drop off.
Diagnostics	Stuck on “why didn’t this push land for these iOS users yesterday?” Hand it to the assistant. It traces the message, correlates platform settings and the preset, and explains what to fix in plain English.
Analytics and revenue	Build the dashboard your CMO actually asks for, compare a journey’s revenue lift against the global control group, and break a campaign down by app, country, or cohort without writing a single SQL query.
Integrations	Connect Stripe and start segmenting by payment events the same day, sync Meta for cross-channel retargeting, plug in Segment, Piano, Hubspot, Shopify, or a custom webhook, and check the connection status from the chat.
Vouchers and rewards	Drop ten thousand promo codes into a pool, wire it to your Welcome journey, and watch redemption stats land. This is handy for launches, win-back campaigns, and partner promotions.

Important

Sending live messages from an AI client is intentionally not exposed through the MCP server to prevent accidental broadcasts. Use the assistant to plan and build your campaigns. Trigger the actual broadcast from the Pushwoosh Control Panel or from a Customer Journey you have published.

Use cases

Build a campaign end-to-end from your editor

Ask your assistant: “In application XXXXX-XXXXX, create a segment of users who added a product to the cart in the last 7 days but did not purchase. Then create a push preset ‘Cart Reminder — discount 10%’ and a Customer Journey that targets this segment, sends the preset 1 hour after entry, and exits if a Purchase event is recorded.”

Your assistant assembles the segment, the preset, and the journey. You review and publish the result in the Pushwoosh Control Panel.

Diagnose why a push did not arrive

While debugging in your AI client, ask: “In application XXXXX-XXXXX, why did message m-12345 fail for device d-67890? Explain in plain English.” The assistant investigates the delivery, correlates platform settings and the message preset, and explains the failure plus suggested fixes.

Analyze and report on revenue

Ask: “Compare revenue and conversion for users who completed Journey Welcome Flow versus the global control group over the last 30 days, broken down by application.” The assistant assembles the comparison from your project’s data so you can keep iterating on the analysis or paste the result into your weekly report.

Onboard a new project from a brief

Provide a short product brief and ask the assistant to “create the application, add iOS and Android platforms, set up an Email From address, register a test device, create the standard Welcome and Cart Abandonment journeys with sensible defaults, and list everything you created so I can review.”

Compatible AI clients

The ManyMoney AI MCP server works with any client that implements the MCP specification, including:

• Claude Desktop by Anthropic
• Cursor and Windsurf
• Cline and Continue
• ChatGPT Desktop with MCP support enabled
• Custom integrations built on top of the MCP specification

Connect the MCP server

Step 1. Make sure you have a Pushwoosh account

You need an active Pushwoosh account with access to the applications you want the assistant to work with. The MCP server runs every action under your Pushwoosh permissions, and your AI client can only do what you can do in the Control Panel.

Step 2. Add the server to your AI client

Use the following endpoint in your client’s MCP configuration:

https://manymoney.svc-nue.pushwoosh.com/mcp

Claude Desktop

Add the server to your Claude Desktop config file (claude_desktop_config.json):

{
  "mcpServers": {
    "manymoney": {
      "url": "https://manymoney.svc-nue.pushwoosh.com/mcp"
    }
  }
}

Restart Claude Desktop. On first use, the app opens a browser window so you can sign in to Pushwoosh.

Cursor / Windsurf

Add the server to your .cursor/mcp.json (or the equivalent file in Windsurf):

{
  "mcpServers": {
    "manymoney": {
      "url": "https://manymoney.svc-nue.pushwoosh.com/mcp"
    }
  }
}

Reload the editor. The first request triggers a browser sign-in to Pushwoosh.

Other clients

For any other MCP-compatible client, point it at https://manymoney.svc-nue.pushwoosh.com/mcp and follow the client’s instructions for adding a remote MCP server.

Step 3. Authenticate

The MCP server supports two authentication methods:

Method	When to use it
OAuth 2.0	Recommended for desktop and editor clients. The client opens a browser, you sign in with your Pushwoosh account, and the token is stored locally.
API token	Recommended for headless or scripted setups. Send the header Authorization: Token <your API token> with every request. Create and manage API tokens in the Pushwoosh Control Panel.

Step 4. Try it out

Open a new chat in your AI client and ask something concrete and scoped to one of your applications:

In application XXXXX-XXXXX, list the customer journeys created in the last 30 days, and for each one show whether it is currently running and how many users entered it.

If the connection works, your assistant pulls the data from your project and returns a grounded answer.

Tips and best practices

• Provide application codes and identifiers. The same prompting tips that work in the in-product ManyMoney AI work over MCP. Reference applications by code, journeys by UUID, and segments by name to keep the assistant scoped.
• Verify destructive actions. Before approving a tool call that deletes data, archives a journey, or rewrites a segment, review the parameters. ManyMoney AI also asks for an explicit confirmation phrase for high-impact operations.
• Keep the connection scoped per project. Most MCP clients let you enable or disable individual servers per workspace. If you only want Pushwoosh available in a specific repo or document, configure it there.
• Stay in your safety zone. Sending live messages is intentionally not available over MCP. Plan, build, and review with the assistant. Trigger the actual broadcast from the Pushwoosh Control Panel or an automated journey.

Data and your AI provider

The MCP server runs every action under your Pushwoosh permissions and applies the same rate limits and account scoping as the Control Panel. It does not, however, control what your AI client sends to its model provider. Your prompts, the tool arguments your assistant builds, and the Pushwoosh API responses it reads all flow through the AI client and the model vendor you have chosen — Anthropic, OpenAI, or whoever your client uses — and their privacy, retention, and training policies apply.

If you need Pushwoosh to handle the model side of the conversation under its own data handling rules, use ManyMoney AI in the Control Panel instead. There, Pushwoosh routes prompts only through authorized subprocessors and applies the data handling described on the ManyMoney AI page.

Related

ManyMoney AI inside the Control Panel

Troubleshooting with ManyMoney AI

AI tools overview