Create context-aware research questions and establish a tracking system for analyzing architectural decision records (ADRs) using a knowledge graph and research objectives.
Manage system extensions and advanced features including mesh visualization, receptor management, constructed inventories, labels, debug tools, credential sources, approval workflows, and bulk operations.
Generate research questions and create tracking systems for architectural analysis projects using context-aware inputs like knowledge graphs and research objectives.
Perform targeted web research, generate intelligent suggestions, and store findings in memory for efficient task management. Combines web research with local knowledge caching to enhance research workflow.
Create a structured Knowledge Matrix sheet with proper headers to organize revenue tracking data before using other Matrix tools in the Revenue Engine MCP system.
Enables LLMs to read, search, and manage Obsidian vault markdown files, including YAML frontmatter, wikilinks, and graph operations through a secure stateless I/O layer.
Enables AI development tools to maintain context across chat sessions with automatic branching, progress tracking, and TODO management for different tasks.
An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.