Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.

A NestJS module for easily exposing AI tools, resources, and prompts via the Model Context Protocol (MCP), supporting multiple transport types and automatic discovery and registration functions.

This project is an MCP server for managing memory text files, helping AI models like Claude maintain context between conversations. It provides functions to add, search, delete, and list memories, supporting exact matching operations based on substrings. It is designed to store memories in simple text files, similar to ChatGPT's memory mechanism, and triggers memory storage through prompts and training.

An MCP server based on Claude AI for optimizing programming prompts and enhancing the usage experience in AI IDEs such as Cursor.

GistPad MCP is a Node.js-based server for managing personal knowledge, daily notes, and reusable prompts via GitHub Gists. It works with the VS Code extension and the GistPad.dev website, supporting access to and editing of Gists from any MCP-compatible AI product.

Achieve AI - driven design automation in Figma through natural language prompts

CC-Meta is a Claude code prompt iteration tool within the terminal that helps optimize the clarity, completeness, and effectiveness of prompts through instant AI feedback.

Promptheus is an intelligent prompt optimization tool that helps users optimize LLM prompts and improve the effect of AI conversations through adaptive questioning and multi - model support.

The MCP Image Generator is an image generation service based on Together AI or Replicate, supporting access through an SSE endpoint or local running, and providing the function of generating images based on text prompts.

The MCP Prompts Server is a service system for managing and applying AI prompt templates, supporting multi-format conversion, variable replacement, and resource integration. It can work in collaboration with other MCP services.

The Clockify MCP service is a server integrated with AI tools for managing time records in Clockify. Users can record time entries by sending prompts to large - language models.

OpenAPI-MCP is a proxy server that can dynamically convert OpenAPI specifications into standardized MCP tools, resources, and prompts, simplifying the integration process between AI agents and external APIs.

PromptLab is an intelligent system that optimizes basic user queries into AI system prompts through MLflow integration, providing dynamic template matching and parameter extraction functions.

The AI Studio MCP Server is a model context protocol server integrated with Google AI Studio/Gemini API, providing content generation functions that support files, conversation history, and system prompts.

The Azure CLI MCP Server is a server based on the MCP protocol. It enhances the functions of Azure CLI by wrapping it and providing friendly prompts, and supports the execution of Azure CLI commands driven by AI.

The prompts.chat MCP server allows AI coding assistants to directly access thousands of AI prompts, supporting searching for, obtaining, and saving prompts, and providing a variable replacement function.

The Model Context Protocol (MCP) is an open - source protocol released by Anthropic, aiming to solve the standardization problem of connecting large language models (LLMs) with external data sources and systems. It provides a structured framework that enables models to integrate and utilize external context, thereby expanding their capabilities and improving response accuracy. MCP supports functions such as knowledge expansion, external tool calls, and pre - written prompts.

An MCP server based on the Rust library, used to generate context prompts from codebases and help AI assistants better understand and process code repositories.

The game asset generator uses AI models and the MCP protocol to quickly generate 2D and 3D game resources through text prompts.

An MCP server based on Google Gemini's image generation model that allows AI agents to generate, edit, and describe images through text prompts, supporting multiple models and configuration options.

A general-purpose Model Context Protocol (MCP) server that allows any LLM or AI agent to access expert skills in the local file system. Enables cross-platform skill sharing through structured prompts and supports lazy loading to save context tokens.

An AI prompt management service based on TypeScript, adopting the MCP protocol and pnpm monorepo architecture.

Modex is a Clojure library that implements the Model Context Protocol (MCP) for building MCP servers and clients, providing AI models with the ability to extend tools, resources, and prompts. It supports communication via standard input and output (stdio) without additional proxy conversion, and is suitable for enhancing AI functions such as database queries.

The HomeyPro MCP server is a model context protocol service for interacting with the HomeyPro smart home system. It provides paginated access and comprehensive management functions for devices, zones, and processes, including features such as AI intelligent prompts and resource caching.

An MCP - protocol chat server that supports multiple AI vendors, allowing requests to be sent via tools or preset prompts

mcpn.ai is a tool that helps users create, organize, and share AI workflows and prompts. It is similar to a dynamic prompt library that can be shared and version - controlled across MCP tools and servers.

An MCP service that automatically optimizes AI prompts based on evolutionary algorithms, iteratively improving prompt performance through genetic algorithms

The medRxiv MCP Server is a bridge tool connecting AI assistants and the medRxiv preprint library, enabling functions such as searching for preprints, retrieving metadata, and accessing content in the field of health science through the Model Context Protocol (MCP). It supports local storage and paper analysis prompts.

Flux ImageGen MCP Server is a dedicated server based on the Model Context Protocol (MCP), focusing on providing image generation and processing functions through Pollinations AI. It supports generating image URLs from text prompts, directly generating images, and managing multiple image generation models.

MockLoop MCP is the world's first AI-native API testing platform integrating the Model Context Protocol (MCP). It revolutionizes API testing through AI-driven scenario generation, automated test execution, and intelligent analysis capabilities. The platform offers 5 AI prompts, 15 scenario resources, 16 testing tools, and 10 context tools, supports a dual-port architecture and stateful testing workflows, and is suitable for enterprise-level compliance and advanced analysis requirements.

The Pyth Network MCP Server provides real-time access to decentralized oracle price feeds, supporting price data for over 1,930 assets, including cryptocurrencies, stocks, foreign exchange, etc. It provides market data services for AI agents through 5 tools, 4 prompts, and 3 resources.

This project includes the MCP server and VSCode extension, which generates HTML, CSS, and JavaScript applications through natural language prompts and uses Google LLM technology to achieve AI code generation.

The promptz.dev MCP server provides the function of directly accessing prompts on the promptz.dev platform from an AI assistant, reducing context switching in development.

The LangChain MCP server provides seamless access to the LangSmith prompt library, containing over 1000 community-verified AI prompts, supporting advanced search, metadata viewing, and version control, and is compatible with multiple MCP clients.

Layer Prompt Manager is a tool for saving, versioning, and managing custom AI prompts in code repositories, which can be seamlessly integrated with AI-driven IDEs.

This project implements a Model Context Protocol (MCP) server for standardizing the connection between AI applications and data sources, supporting querying document content and providing three core capabilities: resources, tools, and prompts.

Create an AI web automation project for the application of AI prompts and MCP servers in automated testing

This is a prompt optimization server based on the Model Context Protocol (MCP) that uses AI to enhance and clean the original prompts, making them clearer, more actionable, and more effective. It supports local and cloud LLMs and provides quality assessment and retry strategies.

Overview: The Model Context Protocol (MCP) is a protocol designed to enable AI agents to access and utilize context, tools, and prompts. It allows for the creation of servers that provide standardized interfaces to interact with local or remote services, thereby enabling automated workflows such as reading files, operating databases, debugging code, and pushing changes to GitHub.

Overview: MCP (Model Context Protocol) is a framework for AI automation through a standardized protocol, which includes key components such as hosts, clients, servers, local data sources, and remote services. The core lies in the server, which provides context, tools, and prompts for clients. MCP enables AI agents to automate tasks, such as code generation, debugging, and interaction with external services like GitHub repositories.