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Why this server?
Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings.
Why this server?
Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.
Why this server?
Provides vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.
Why this server?
Provides intelligent summarization capabilities through a clean, extensible architecture. Mainly built for solving AI agents issues on big repositories, where large files can eat up the context window.
Why this server?
An open standard server implementation that implements Anthropic's Model Context Protocol to enable seamless integration between LLM applications and RAG data sources using Sionic AI's Storm Platform.
Why this server?
🔍 A Model Context Protocol (MCP) server providing unified access to multiple search engines (Tavily, Brave, Kagi), AI tools (Perplexity, FastGPT), and content processing services (Jina AI, Kagi). Combines search, AI responses, content processing, and enhancement features through a single interface.
Why this server?
A server that allows AI assistants to browse and read files from specified GitHub repositories, providing access to repository contents via the Model Context Protocol.
Why this server?
A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.