Provides knowledge graph functionality for managing entities, relations, and observations in memory with strict validation rules to maintain data consistency.
Fetches and extracts comprehensive package documentation from multiple programming language ecosystems (JavaScript, Python, Java, etc.) for LLMs like Claude without requiring API keys.
Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.
The MCP Server integrates APIs from the Youtube-Summarizer as tools within the MCP protocol, allowing for local AI application interaction and tool utilization through natural language queries.
Connects a RAG application to open-webui using Model Context Protocol (MCP), enabling server-to-client communication for context retrieval and tool usage in remote environments through Server-Sent Events (SSE).
Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.