MCP Server for SendPulse Chatbots

This project is an implementation of a Model Context Protocol (MCP) server designed to work with the SendPulse Chatbots API. It allows Large Language Models (LLMs) like those from OpenAI to interact with the SendPulse API through a standardized set of tools.

This server is built with TypeScript and runs on Node.js using the Express framework.

Features

The server exposes a combination of global and universal, channel-specific tools to the LLM.

Global Tools

These tools provide general, account-wide information.

• get_account_info: Returns information about the current SendPulse account, including pricing plan, message counts, bots, contacts, etc.

• get_bots_list: Returns a list of all connected chatbots with details for each.

• get_dialogs: Returns a list of dialogs from all channels, with support for pagination and sorting.

Universal Tools

These tools perform actions on specific channels. They require a channel parameter to be specified.

• send_message: Sends a text message to a contact.

  • channel: The channel to use. Supported values: whatsapp, telegram, instagram, messenger, livechat, viber.

  • contact_id: The ID of the recipient.

  • text: The message content.

Related MCP server: Wassenger WhatsApp MCP Server

Authentication

The server supports two flexible methods for authenticating requests to the SendPulse API, which are handled on a per-session basis.

Method 1: API ID & Secret (Recommended)

The client can provide SendPulse API credentials by sending two custom HTTP headers:

• x-sp-id: Your SendPulse API ID.

• x-sp-secret: Your SendPulse API Secret.

Upon receiving these headers, the MCP server will automatically perform the OAuth 2.0 client_credentials flow to obtain a temporary access token from SendPulse. These tokens are cached in memory to improve performance for subsequent requests from the same user (same API ID).

Method 2: Direct OAuth Token

The client can provide a pre-existing, valid SendPulse OAuth 2.0 token directly. This is supported in two ways:

1. Via Authorization Header (Standard):

   • Authorization: Bearer <your_oauth_token>

2. Via MCP initialize Request Body (Legacy/Compatibility):

   • As part of the MCP JSON configuration.

Getting Started

Prerequisites

• Node.js (v18 or later recommended)

• npm (usually comes with Node.js)

Installation

1. Clone the repository (if applicable).

2. Install the project dependencies:

   npm install

Build

To build the project, run the following command.

npm run build

Running the Server

Once the project is built, you can start the server:

npm start

You should see a confirmation message in your console: SendPulse MCP HTTP Server running on http://localhost:3000/mcp

Exposing the TEST Server with ngrok

To make your local server accessible to services like the OpenAI sandbox, you need to expose it to the internet. You can use ngrok for this purpose. Open a new terminal window and run:

ngrok http 3000

Ngrok will provide you with a public https:// URL (e.g., https://random-string.ngrok-free.app). Use this URL (https://random-string.ngrok-free.app/mcp) when configuring the MCP tool in your LLM client.

Note: To bypass the ngrok browser warning page, you may need to configure your LLM client to send an additional header with every request, for example: ngrok-skip-browser-warning: "true".