MCP OI-Wiki

Enables semantic search and natural language retrieval of documentation by indexing content from URLs or local files into a vector database using Model Context Protocol integration.

Provides Model Context Protocol-based vector database services with semantic document search, metadata and content filtering, persistent storage, and comprehensive CRUD operations for efficient document management.

Provides advanced web crawling, content vectorization, and retrieval-augmented generation (RAG) capabilities for AI agents and coding assistants using the Model Context Protocol, enabling efficient multi-source data indexing, semantic search, and configurable embedding models.

Provides real-time semantic search across files by watching directories, creating vector embeddings, and enabling efficient querying and indexing through the Model Context Protocol.

Enables crawling websites to generate searchable Markdown documentation with semantic chunking and vector embeddings, providing efficient Model Context Protocol tools for document listing, heading retrieval, and semantic search integration.