learning & documentation向けの厳選されたMCPサーバーコレクションをご覧ください。1661個のサーバーを閲覧し、ニーズに最適なMCPを見つけましょう。
Parses Go source code into an Abstract Syntax Tree (AST) and converts it into a clean, LLM-friendly format to enhance prompt context.
Enables AI code assistants to generate up-to-date code by checking the latest library documentation.
Exposes advanced Notion search, reading, summarization, and emotion-analysis capabilities to any MCP-compatible LLM client.
Serves and queries documentation with advanced AI capabilities for knowledge retrieval and management.
Delivers accessibility acceptance criteria from MagentaA11y via the Model Context Protocol for local or remote access.
Provides comprehensive access to Rust crate documentation and metadata from crates.io and docs.rs.
Explores the fundamentals of protocol design and client-server communication through a learning project.
Provides an intelligent search and retrieval system for Pybricks documentation and code examples.
Generates a Python-based server for educational course management from JSON-defined course content.
Summarizes agent conversations into structured documents for effortless review and retention.
Serves Python coding best practices and guidelines via a Model Context Protocol (MCP) server for general development and FastAPI APIs.
Integrates enterprise-grade AI semantic search with real-time web capabilities to provide comprehensive support for LiveKit documentation.
Summarizes AI chat conversations and organizes them into structured markdown files for IDE users.
Logs Cursor AI chat interactions to Notion for analysis, documentation, and knowledge management.
Implements a JSON-RPC 2.0 real-time server using Node.js and Express, complete with a vanilla JavaScript frontend client for demonstration.
Enables seamless semantic search over AWS Cloudscape documentation, assisting AI agents and coding assistants in retrieving relevant information.
Provides a local Alloy-Language server to facilitate the generation and execution of Alloy modeling code.
Serves remote Markdown files as accessible MCP resources.
Demonstrates building a Model Context Protocol (MCP) server in Python to expose custom tools and resources to large language models (LLMs) and MCP clients.
Integrates BlackArch Linux security tools into a comprehensive server for educational penetration testing using the Model Context Protocol.
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