This project is a Model Context Protocol server designed to bridge the gap between local frontend component libraries and language models. It functions as a development assistant that provides AI tools with the structural context, dependency requirements, and installation patterns necessary to generate accurate, framework-specific UI code.
Les fonctionnalités principales de jpisnice/shadcn-ui-mcp-server sont : Model Context Protocol Servers, MCP Servers, Model Context Protocol Implementations, Generative Development Assistants, Usage Pattern Catalogs, UI Component Documentation Systems, Component Metadata, AI-Driven Schema Extractions.
Les alternatives open-source à jpisnice/shadcn-ui-mcp-server incluent : mark3labs/mcp-go — mcp-go is a Go implementation of the Model Context Protocol (MCP) providing an SDK and framework for building servers… mikeyobrien/ralph-orchestrator — This project is an autonomous workflow engine and orchestration platform designed to coordinate specialized AI agents.… memmachine/memmachine — MemMachine is a centralized memory management server and model-agnostic memory layer for large language models. It… cameroncooke/xcodebuildmcp — xcodebuildmcp is a Model Context Protocol server that exposes Xcode build, test, and device management tools for AI… atmosphere/atmosphere — Atmosphere is a Java-based framework for building and coordinating AI agents. It provides a real-time transport layer… cursortouch/windows-mcp — This is a Model Context Protocol server that exposes Windows desktop automation and system administration functions to…
mcp-go is a Go implementation of the Model Context Protocol (MCP) providing an SDK and framework for building servers that connect large language model applications to external tools and data sources. It serves as a developer kit for implementing bidirectional communication and structured data exchange between AI clients and servers. The framework enables the creation of executable tools with structured output schemas, reusable prompt templates, and data resource exposure via URI templates. It supports multiple transport layers, including stdio, HTTP, and Server-Sent Events, using a transport
MemMachine is a centralized memory management server and model-agnostic memory layer for large language models. It functions as a persistence layer that stores user profiles and conversational context, providing a decoupled data store that prevents vendor lock-in by serving different AI models through a consistent API. The system implements the Model Context Protocol to share persistent agent memories and session data with compatible AI clients. It utilizes a multi-tiered memory hierarchy, combining a graph-based conversation store for episodic interactions with a vector knowledge base for se
This project is an autonomous workflow engine and orchestration platform designed to coordinate specialized AI agents. It functions as a development framework that manages the end-to-end lifecycle of complex, multi-step tasks, including persona definition, persistent memory management, and the execution of automated coding workflows. By acting as a Model Context Protocol server, it enables standardized communication between development tools and external AI models. The platform distinguishes itself through an event-driven architecture that routes typed messages between agent personas, allowin
xcodebuildmcp is a Model Context Protocol server that exposes Xcode build, test, and device management tools for AI coding agents to automate iOS and macOS development workflows. It operates as a background daemon per workspace, communicating tool requests and responses over standard input/output using JSON-RPC messages, and streams progress and results as newline-delimited JSON objects for machine parsing. The project provides an interactive setup wizard and file-based client configuration to install skill files into predefined directories for supported AI coding clients. It manages the full