Optimized Memory
This optimized memory MCP server, developed by Herman Wong, provides a persistent knowledge graph for AI systems using SQLite as a backend. Built with Python and leveraging libraries like aiofiles and mcp, it offers tools for creating, updating, and querying entities, relations, and observations in a graph structure. The server is designed for efficient memory management and seamless integration with Claude Desktop. By abstracting knowledge storage and retrieval into a standardized MCP interface, it enables AI assistants to maintain context and personalize interactions across conversations. This implementation is particularly useful for applications requiring long-term memory and relationship modeling, such as personalized chat systems, knowledge management tools, or AI-powered personal assistants.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Author type
- human
- Updated
- 2026-04-25
Use via MCP
Resolve Optimized Memory from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.