"Generating Custom Messaging for Users" matching MCP servers:

MCP server that serves tools for interacting with a users API over stdio, supporting operations like finding users. It provides a validated configuration and structured logging for building MCP-based user management workflows.

A comprehensive MCP server with 30+ custom tools organized into categories: date/time operations, file management, system information, text processing, and web operations. Enables async communication with robust error handling and flexible CLI integration.

A full-power MCP server for n8n that enables AI agents to create, read, update, delete, and test workflows and credentials, including webhook testing, validation, and backup/versioning.

A Next.js-based MCP server that provides basic utility tools including echo, current time retrieval, and safe mathematical calculations through HTTP and SSE transports.

A user management MCP server that enables CRUD operations on users stored in a JSON file, with random user generation using Faker.

Enables AI coding agents to read Figma file hierarchies, node properties, and rendered images locally via MCP, using a personal access token without Figma's hosted MCP integration.

A Middleware/Control Plane server that allows AI code agents to securely execute queries against various databases (PostgreSQL, MySQL, MongoDB, Oracle) without directly exposing credentials.

Enables reading, replacing, and toggling a Salla store's custom CSS and JavaScript through the theme editor's advanced customization endpoints, using a logged-in local Chromium profile with backups and verification.

Dynamically fetches and exposes OpenAPI/Swagger documentation as tools for AI assistants, enabling them to explore and interact with any REST API.

Custom MCP server for n8n automation, providing 27 tools to manage workflows, executions, credentials, variables, and more via the n8n REST API. Designed to fix compatibility issues with Antigravity AI assistant.

A Model Context Protocol server that transforms text into structured JSON data using templates with placeholders.

Enables MCP clients to drive your already-open Chrome browser like a human, using 45 tools for navigation, perception, capture, and trusted input that pages receive as genuinely user-generated.

Provides integration with GitLab repositories to manage files, issues, merge requests, and branches through the Model Context Protocol. This implementation includes specific fixes for schema validation issues found in standard GitLab MCP servers.

Enables AI agents to perform full CRUD operations on GitHub, including repositories, issues, pull requests, branches, comments, reviews, and merges through the Model Context Protocol.

Browser-backed QA MCP server that lets coding agents test web apps, collect browser evidence, and return fix-ready reports before shipping.

Enables full CRUD operations on HubSpot custom objects, complementing the official server that only handles standard objects.

Enables Codex to consult Claude Code for a second opinion through a local stdio MCP server, exposing the ask_claude tool for text-only reviews and recommendations using existing Claude Code authentication. Codex can request findings and tradeoff analysis without allowing Claude to edit files or run commands.

Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.

Odds are, most of what you want to build is already open source. Tell your AI agent what you're making, and it finds it: which repos to use as they are, which to adapt, and what's left for you to write. Every repo it names is real, licensed for your use or flagged, and checked live.

Exposes the Hellio Messaging API as MCP tools for SMS, OTP, voice, HLR lookup, email verification, balance, pricing, webhooks, and USSD operations.

Enables AI coding agents to discover and verify current open-source libraries and models using live GitHub activity, PyPI adoption, license checks, and hardware VRAM estimates before generating code.

A CDP-native MCP server that lets AI agents drive a real Chrome browser with deep network/console inspection, robust page interaction, and the ability to reverse-engineer a site's API flow into runnable code with dependency chaining and replay verification.

A live MCP server that connects AI assistants to a running Obsidian instance, enabling full app automation including reading notes, clicking UI elements, managing plugins, and executing JavaScript.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.

MCP server that launches Mesen/MesenCE headlessly and exposes the emulator's debugger-face Lua API over MCP JSON-RPC, enabling AI agents to load ROMs, step frames, inspect memory/registers, manage breakpoints/watches, trace execution, and export Code/Data Logger maps for SNES, NES, PC Engine, and Game Boy Advance.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.

Enables Codex and other MCP clients to read kickoff cards, acceptance criteria, Git evidence, and next steps, then append validated, audited progress events for resumable project handoffs.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.