Building a Knowledge Graph for Personal Life Organization – MCP tools

create_knowledge_project
Graforest MCP
Create a Neo4j knowledge graph project with optimized schema for organizing topics, articles, authors, and concepts. Deploys to staging for building searchable knowledge bases from text.
validate_graph
Enhanced Knowledge Graph Memory Server
Check knowledge graph data for integrity issues to maintain reliable storage in the Enhanced Knowledge Graph Memory Server.
MIT
add_location
MemoryMesh
Add a new location to MemoryMesh's knowledge graph, including details like name, type, description, status, notable features, and associated characters, quests, or artifacts for enhanced world-building.
MIT
search_nodes
Obsidian Memory MCP
Search for entities, types, and observation content in a knowledge graph stored in Obsidian Memory MCP. Use a query to retrieve relevant nodes for enhanced knowledge graph navigation and analysis.
MIT
create_entities
MCP DuckDB Knowledge Graph Memory Server
Add multiple entities to a knowledge graph by specifying names, types, and associated observations for enhanced data organization and retrieval.
MIT
create_entities
RAG MCP Server
Add multiple entities to a knowledge graph for semantic code indexing and search, enabling structured data organization and relationship management.

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AWS Knowledge
The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
Knowledge Raven
Make your knowledge agent-ready. Connect docs from Confluence, Notion, GitHub, Dropbox, or Google Drive — any AI agent searches them via one MCP endpoint. 3 retrieval modes: vector search, broad search, and full document access. The agent decides how deep to dig.