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    • A
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      A personal memory system that provides AI assistants with long-term memory capabilities through semantic search and vector storage. It enables Claude Code to store, retrieve, and manage personal context and project preferences using flexible LLM backends.
      8
      MIT
    • A
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      Provides persistent cross-session memory for Claude and other AI agents, letting users store decisions, patterns, and knowledge once and retrieve them automatically across future conversations while surviving context limits and session restarts.
      17 npm
      MIT
    • A
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      Persistent memory management for AI assistants like Claude, enabling creation, search, and retrieval of memories with tagging and triggers.
      10
      MIT
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      Persistent memory for Claude Code — a self-evolving knowledge layer that survives across sessions, grows from every conversation, and surfaces relevant context automatically.
      14
      MIT
    • A
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      Provides persistent cross-session memory for Claude and other AI agents, enabling users to store decisions, patterns, and knowledge once and automatically retrieve relevant context in future conversations across sessions and workspaces.
      17 npm
      MIT

    TDQS

    B3.2/5.0

    Scored across 18 tools

    Disambiguation4/5

    Most tools have distinct purposes, but there is some overlap between 'storeAssistantMessage'/'storeUserMessage' and 'endConversation'/'initConversation', which bundle similar operations. The 'manageVector' tool is broad but clearly defined, while others like 'getRecentEpisodes' and 'getRecentMessages' target different memory types, reducing confusion.

    Naming Consistency3/5

    Naming is mixed with camelCase (e.g., 'checkHealth') and snake_case (e.g., 'store_assistant_message'), though most tools use a verb_noun pattern. Inconsistencies like 'diagnoseVectors' vs. 'manageVector' and variations in verb usage (e.g., 'get', 'store', 'record') detract from a uniform convention.

    Tool Count4/5

    With 18 tools, the count is slightly high but reasonable for a memory system server covering health, diagnostics, conversation management, and multiple memory types (short-term, episodic, long-term). It avoids being overwhelming by grouping related operations, though some tools could be consolidated.

    Completeness5/5

    The tool set comprehensively covers the memory system domain, including initialization, conversation handling, message storage, episode recording, milestone/requirement/decision tracking, vector management, health checks, diagnostics, and context retrieval. There are no obvious gaps, supporting full lifecycle operations for memory management.

    Maintenance

    ActivityInactive
    ResponsivenessLimited data