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Twilio's official MCP server, built by the Twilio Alpha team and published as @twilio-alpha/mcp, exposes the entirety of Twilio's public API surface to AI assistants over the Model Context Protocol. Rather than hand-writing tool wrappers, the server auto-generates MCP tools directly from Twilio's OpenAPI specs, so it stays in sync with new Twilio products (Voice, Messaging/SMS, Verify, Lookup, Conversations, and more) as they ship. Because a full API surface can blow past an LLM's context window, the server supports --services and --tags flags to scope which Twilio products are loaded into a given session, keeping tool lists small and relevant. Authentication uses a Twilio Account SID paired with an API Key/Secret pair (created in the Twilio Console), passed as a single credential string at launch rather than long-lived account credentials. The monorepo also ships a companion openapi-mcp-server package that can turn any OpenAPI spec into an MCP server using the same generator, useful for teams building on top of Twilio's partner or vertical APIs. Twilio's security guidance explicitly recommends against running community MCP servers alongside the official one to reduce the risk of a compromised third-party tool touching production SMS, voice, or verification workflows tied to real phone numbers and customer data.

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The Resend MCP Server is Resend's official Model Context Protocol integration, letting AI assistants send and receive transactional email, manage contacts, broadcasts, and domains directly from Claude, Cursor, or any MCP-compatible client. Resend offers two ways to connect: a fully hosted remote server at mcp.resend.com (Streamable HTTP, no local install, OAuth login on first connect) that works well for Claude web/desktop and Cursor, or the same open-source code run locally via `npx resend-mcp` (published to npm as `resend-mcp`) using stdio or HTTP transport for CI, headless agents, or self-hosted setups. Authentication is either OAuth (hosted mode, browser-based) or a Resend API key passed as a Bearer token — handy for servers where a browser login isn't possible. Setup is documented for Claude Code, Claude Desktop/web, Cursor, Windsurf, Codex, and GitHub Copilot in VS Code. Typical use: ask Claude to "send a welcome email to this new signup using our verified domain" or "list contacts added to the newsletter audience this week," and the MCP server routes the request through your existing Resend account — no custom SMTP or REST integration code required. With 546+ GitHub stars, it's one of the most widely adopted first-party MCP servers in the email/transactional-messaging category.

The Discord MCP Server (SaseQ/discord-mcp) is a Java-based Model Context Protocol integration built on JDA (Java Discord API) that turns a Discord bot into a set of tools AI assistants can call directly. It lets Claude, Cursor, and other MCP clients read and send channel messages, manage threads, create and moderate channels and categories, manage roles and permissions, look up server (guild) members, and pull message history — enabling AI-driven community moderation, automated announcements, and Discord-native workflows without hand-writing bot code. The recommended install is Docker: set DISCORD_TOKEN (from a bot registered in the Discord Developer Portal) and an optional DISCORD_GUILD_ID as environment variables, then run the saseq/discord-mcp:latest image with SPRING_PROFILES_ACTIVE=http, which exposes the MCP endpoint at http://localhost:8085/mcp for remote/HTTP-transport clients. A Docker Compose path and a stdio-transport mode are also documented for local, per-client setups like Claude Desktop. Because it wraps JDA (Spring Boot underneath), it handles Discord's gateway/rate-limit quirks for you rather than requiring a hand-rolled REST client. This is the most-starred dedicated Discord MCP implementation; other community servers like v-3/discordmcp and hanweg/mcp-discord cover similar ground with lighter Node/Python stacks if a non-JVM runtime is preferred.

A Telegram MCP server that connects Claude, Cursor, and other MCP-compatible clients directly to a Telegram user account via the Telethon library, exposing 80+ tools grouped into accounts, chats/groups, messages, contacts, media, profile/privacy, and folders/drafts. It supports multi-account setups (list accounts and route tool calls by label), full group and channel administration (create/join/leave, invites, bans, admin roles, slow mode, topics), rich messaging (send, schedule, edit, delete, forward, pin, reply, search, polls, reactions, inline button inspection and press), contact management (add/remove/block/import/export), and media handling (files, voice notes, stickers, GIFs, downloads/uploads). All tool results containing Telegram user-controlled content are sanitized before being returned as structured JSON, and a device-identity feature lets the session appear as a distinct, labeled client rather than a generic script. Authentication uses a Telegram API ID/hash from my.telegram.org plus a generated session string — no bot token required, since this operates as a full user account. A `TELEGRAM_EXPOSED_TOOLS=read-only` mode is available to restrict the MCP surface to read-only tools when write access is not needed. The project explicitly warns against installing the unrelated `telegram-mcp` package from PyPI, which is owned by a different, unaffiliated project — always install from source via `uv sync`.

The WhatsApp MCP server connects Claude and other MCP clients directly to your personal WhatsApp account so an AI assistant can search, read, and send messages, contacts, and media through natural language. Unlike hosted bots that require the paid WhatsApp Business API, this community server (lharries/whatsapp-mcp, the most-adopted WhatsApp MCP server on GitHub) links to your regular account via the WhatsApp Web multidevice API using the Go whatsmeow library — you authenticate once by scanning a QR code, then re-authenticate roughly every 20 days. It runs as two components: a Go bridge that maintains the WhatsApp connection and stores your full message history in a local SQLite database, and a Python MCP server that exposes tools to the agent. Messages stay on your machine and are only sent to the LLM when a tool explicitly reads them, so you control what the model sees. Available tools cover searching contacts and chats, reading individual and group conversations (including images, video, documents, and audio), sending text messages to people or groups, and sending media — with optional FFmpeg-based conversion to play audio as native WhatsApp voice notes. Note the "lethal trifecta" caution in the README: because it combines private data, tool access, and external content, prompt injection could risk data exfiltration.