Discover our curated collection of MCP servers for learning & documentation. Browse 1661servers and find the perfect MCPs for your needs.
Registers a system information tool within an MCP service, enabling remote execution via `cline`.
Serves comprehensive data about Colombia, including departments, regions, and cities, via a Model Context Protocol server for educational and practical implementations.
Provides an AI-powered web platform for interactive learning of discrete mathematics concepts.
Serves documentation for applications using the Model Context Protocol (MCP).
Reverse UTF-8 text while preserving grapheme clusters, operating as a minimal server via stdio.
Explores local file systems, demonstrating core Model Context Protocol (MCP) concepts for learning and development.
Track real-time AI/LLM research advancements across multiple academic and development platforms.
Enables AI assistants to access Go documentation and package listings via the Model Context Protocol.
Implements a basic Model Context Protocol (MCP) server in TypeScript, following official specifications and best practices.
Enables AI assistants to securely connect and interact with your e-books on KOReader, providing real-time access to content and functionality.
Provides current local datetime information in a clean single-line format.
Provides an MCP server for AI assistants and users to access and comprehend the 'Gentleman Programming' book's 18 chapters on software architecture.
Assists developers with integrating Vonage API capabilities into their applications using AI code generation.
Showcases a foundational Model Context Protocol server implementation with built-in tools and resources.
Indexes code repositories using semantic embeddings to provide intelligent search and analysis capabilities for LLM clients.
Provides AI assistants with access to Payman AI's documentation for efficient developer integration.
Generate stunning syntax-highlighted code screenshots effortlessly for professional sharing and documentation.
Connects saved Noverload content to AI assistants, enabling advanced search, content synthesis, and intelligent token management through the Model Context Protocol (MCP).
Generate structured JSON documentation from Java source code or crawl existing HTML Javadoc for intelligent search capabilities.
Facilitates direct documentation search for large language models.
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