MCPSERV.CLUB
samihalawa

SMTP Email MCP Server

MCP Server

Send rich, templated emails via MCP

Active(71)
7stars
1views
Updated Sep 1, 2025

About

An MCP server that manages SMTP configurations and email templates, enabling single or bulk email sending with HTML support and dynamic template variables for AI assistants.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

SMTP Email MCP Server

The SMTP Email MCP Server bridges the gap between AI assistants and real‑world email communication. In many production environments, an assistant must reach users via email—whether for notifications, onboarding, or transactional messages. By exposing a rich set of SMTP‑related tools over the Model Context Protocol, this server lets Claude or any MCP‑compatible client send, manage, and audit email traffic without leaving the AI workflow.

Problem Solved

Traditionally, integrating email into an AI‑driven application requires developers to embed SMTP libraries, manage credentials, and handle batching or rate limits manually. This adds complexity, increases the attack surface for credential leaks, and forces developers to maintain separate code paths for templating and logging. The SMTP Email MCP Server abstracts all of that behind a simple, declarative interface: the assistant can request “send an email” or “update a template” and trust that the server will handle configuration, security, and compliance concerns.

Core Value

For developers building AI assistants that need to communicate with users, this server provides a turnkey solution. It eliminates the need for custom SMTP wrappers, centralizes configuration in one place, and exposes a consistent set of tools that can be called from any MCP‑enabled client. Because the server itself logs every operation, developers gain auditability and can troubleshoot delivery issues without digging into application logs.

Key Features

  • Multiple SMTP Configurations – Store and switch between different mail servers (e.g., production, staging) with a single call.
  • Template Management – Create reusable HTML templates and inject dynamic data via template variables, reducing duplication.
  • Bulk Sending with Batching – Send to thousands of recipients safely by controlling batch size and inter‑batch delay, protecting against provider throttling.
  • Comprehensive Logging – Every send action is recorded, enabling traceability and compliance reporting.
  • Fine‑grained Control – Optional parameters for CC, BCC, and custom “from” addresses give developers full flexibility.

Use Cases

  • User Onboarding – An assistant can automatically send welcome emails with personalized content.
  • Transactional Notifications – Order confirmations, password resets, or appointment reminders can be dispatched reliably.
  • Marketing Campaigns – Bulk email tools support newsletters and promotional blasts while respecting rate limits.
  • Compliance Audits – Logs provide evidence for regulatory reviews, such as GDPR or HIPAA email handling.

Integration with AI Workflows

In an MCP‑centric architecture, the assistant defines a prompt that requires sending an email. It calls the or tool, passing recipient data and template identifiers. The server resolves the appropriate SMTP configuration, renders the template with supplied variables, and transmits the message. The assistant can then continue its dialogue based on success or failure responses, enabling dynamic decision‑making (e.g., retry logic or fallback notifications).

Standout Advantages

  • Zero‑configuration SMTP – Credentials are stored securely on the server side, keeping them out of client code.
  • Unified API – All email operations share the same toolset, simplifying training data for AI assistants.
  • Scalable Design – Batching and rate limiting are built in, allowing the server to handle high‑volume campaigns without custom scaling logic.
  • Audit Trail – Built‑in logging removes the need for external monitoring solutions.

By centralizing email logic in an MCP server, developers can focus on the assistant’s intelligence rather than plumbing, resulting in cleaner codebases and faster feature delivery.