"Rich" matching MCP servers:

Enables autonomous creation of fully branded, chart-and-table-rich .docx proposals using company templates, service profiles, and rate cards stored in Google Drive, requiring no manual formatting.

Enables AI agents to create, manage, and search hierarchical plans with phases, tasks, and milestones through a comprehensive planning API. Supports CRUD operations, batch updates, rich context retrieval, and artifact management for structured project planning.

An intelligent tool that automates the setup of new Model Context Protocol (MCP) server projects through a conversational interface. It generates project structures, technical specifications, and context-rich documentation to streamline AI-assisted development in TypeScript or Python.

MCP server middleware for aiogram Telegram bots — 30 tools, 7 resources, 3 prompts covering messaging, rich media, moderation, interactive keyboards, real-time event streaming, rate limiting, permissions, and audit logging.

A framework for building interactive UI applications and React-based widgets for ChatGPT and Claude using the Model Context Protocol. It enables developers to create custom tools and visual components that facilitate rich, bidirectional interaction between AI models and users.

A server that provides rich UI context and interaction capabilities to AI models, enabling deep understanding of user interfaces through visual analysis and precise interaction via Model Context Protocol.

Enables agents to ask users questions through rich, browser-rendered HTML forms and receive structured answers, supporting approvals, choices, free-text, and form inputs.

Enables AI-driven interactions with Minecraft through a WebSocket-based server and MCP protocol, allowing external MCP clients and in-game chat to trigger AI tools.

An MCP server that provides agentic vision capabilities for visual regression testing by capturing and comparing screenshots using Gemini Flash. It enables users to detect UI changes and conduct conversational investigations to distinguish between intended and unintended visual modifications.