WhatsApp Flows API MCP Server – Overview

The WhatsApp Flows API MCP Server is a ready‑to‑deploy Model Context Protocol (MCP) service that bridges conversational AI assistants with the WhatsApp Business API to deliver interactive surveys and customer engagement workflows. By exposing a set of well‑defined resources, tools, and prompts over the MCP protocol, the server allows an AI assistant to orchestrate complex messaging flows without needing direct access to the WhatsApp API credentials or infrastructure. This abstraction is especially valuable for developers who want to embed conversational experiences in their applications while keeping the sensitive WhatsApp integration isolated behind a secure, containerized service.

At its core, the server manages survey creation, distribution, and response collection through a lightweight job queue backed by Redis. When an AI assistant requests the launch of a new survey, the MCP server schedules an asynchronous job that handles bulk messaging, retries, and status tracking. The background workers process these jobs independently of the main MCP service, ensuring that high‑volume customer lists do not block or time out the assistant’s interaction. The server also provides a real‑time dashboard and Redis Commander UI for monitoring job progress, inspecting queue states, and troubleshooting failures—all accessible from the same Docker compose stack.

Key capabilities include modular ERP integration (Business Central, SAP, Oracle, and custom systems), which allows survey responses to be automatically pushed into existing business workflows. The server’s resource model exposes entities such as , , and , while tools expose actions like , , and . Prompts are pre‑configured to guide the AI assistant through common tasks—e.g., "draft a new survey targeting recent buyers" or "retrieve response statistics for the last campaign". The sampling configuration can be tuned to control how many responses are returned per request, making it suitable for both exploratory analysis and production reporting.

Real‑world use cases span customer satisfaction tracking, product feedback loops, compliance surveys, and event registrations. For example, a retail chain can use the server to send post‑purchase surveys to thousands of shoppers via WhatsApp, automatically ingesting the results into SAP for analytics. A healthcare provider could deploy health check questionnaires to patients, with responses routed directly into a custom ERP for follow‑up care. Because the MCP server handles authentication, rate limiting, and retry logic internally, developers can focus on crafting rich conversational flows in the AI assistant without worrying about WhatsApp’s operational constraints.

Integration into existing AI workflows is straightforward: an assistant calls the tool, receives a job ID, and can poll for completion or subscribe to status updates via the MCP protocol. The server’s Dockerized architecture, hot‑reload development mode, and built‑in monitoring tools give teams rapid iteration cycles while maintaining production reliability. Unique advantages include the seamless ERP plug‑in architecture, background job processing that scales horizontally, and a unified dashboard that eliminates the need for separate monitoring stacks. Together, these features make the WhatsApp Flows API MCP Server a powerful middleware layer for any organization looking to leverage AI‑driven messaging at scale.