Enables semantic search and retrieval of documentation using a vector database, allowing users to add and query documentation from URLs or local files using natural language.
Sponsored
Ragdocs is a Model Context Protocol (MCP) server designed to facilitate semantic search and retrieval of documentation. By leveraging a vector database (Qdrant), it allows users to ingest documentation from various sources, including URLs and local files. Once the documentation is stored, users can perform natural language queries to efficiently search and retrieve relevant information. This tool streamlines the process of accessing and understanding extensive documentation, making it a valuable asset for developers, researchers, and anyone working with large volumes of information.
Características Principales
01Supports natural language queries for documentation search.
0278 GitHub stars
03Adds documentation from URLs or local files.
04Lists all available documentation sources.
05Stores documentation in a vector database (Qdrant) for semantic search.
06Integrates with Cline, Roo-Code, and Claude Desktop via Model Context Protocol (MCP).
Casos de Uso
01Enable context-aware search within software documentation.
02Integrate documentation search capabilities into AI assistants and development environments.
03Quickly find specific information within a large codebase documentation.