SmileAPI MCP Server

The SmileAPI MCP server bridges conversational AI models with the SmileAPI messaging platform, enabling seamless delivery of text, images, videos, audio clips, and documents directly from an assistant like Claude. By exposing a set of MCP‑compatible tools—each corresponding to a specific message type—the server removes the need for developers to write custom API wrappers or handle authentication manually. Instead, a model can simply invoke the appropriate tool with JSON parameters, and the server takes care of constructing the SmileAPI request, applying optional delays or typing simulations, and sending the payload to the intended recipient.

At its core, the server solves a common pain point: integrating rich media communication into AI workflows while keeping security and credential management isolated from the model. Developers can store their SmileAPI credentials in environment variables, ensuring that sensitive tokens never reach the assistant’s context. The server then acts as a trusted proxy: it receives a tool call, validates the payload, and forwards it to SmileAPI. This pattern keeps the assistant stateless regarding external services while still allowing dynamic, real‑time interactions with end users.

Key capabilities include:

• Text messaging with optional typing delay and message scheduling.
• Image, video, audio, and document sending, each supporting captions, view‑once flags, and custom file names.
• Delay controls that let the assistant simulate human typing or schedule future delivery.
• A simple command‑line interface for quick testing, allowing developers to invoke tools directly without setting up a full MCP client.

Real‑world scenarios that benefit from this server are plentiful. Customer support bots can push product images or video tutorials to users; marketing assistants can send personalized PDFs or promotional clips; educational agents might deliver lecture recordings or assignment files. Because the server is MCP‑compatible, any model that understands tool calls can tap into SmileAPI’s reach without additional code, making it a versatile component in automated outreach or interactive storytelling pipelines.

In terms of integration, the server exposes its tools via the standard MCP endpoint. A model simply includes a tool call like in its response. The MCP client (e.g., Claude Desktop) forwards this to the server, which translates it into a SmileAPI request and returns the outcome. This tight coupling ensures that AI workflows remain declarative while external service logic stays encapsulated.

What sets the SmileAPI MCP server apart is its focus on rich media delivery within a single, well‑documented interface. Unlike generic webhook services, it offers fine‑grained control over media properties (view‑once flags, captions, file names) and incorporates user‑friendly delay features that enhance conversational realism. For developers building AI assistants that need to engage users via WhatsApp‑style messaging, this server provides a ready‑made, secure bridge that scales from prototype to production with minimal friction.