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    • A
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      A
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      Provides persistent, searchable memory for AI agents across any MCP-compatible client, storing project context, user preferences, and session learnings locally in SQLite with tools to save, retrieve, search, and manage them.
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    • A
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      Provides persistent memory for AI coding agents via MCP, enabling agents to store and semantically recall facts, events, and lessons across sessions, all running locally without cloud dependencies.
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      Provides local-first persistent memory with a typed knowledge graph and bounded multi-hop retrieval via MCP, letting coding agents and local LLM systems store, search, and recall facts across sessions without hosted services or model dependencies.
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      Local-first, governable long-term memory for AI agents. Provides SQLite-backed storage, cross-session recall, and traceable memory corrections through a standard MCP interface.
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    • F
      license
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      quality
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      3
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    TDQS

    A4.2/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool targets a distinct memory operation: bootstrap, search, add, profile, clear, forget, status, and export. Even memory_bootstrap and memory_search are clearly separated by purpose and description.

    Naming Consistency5/5

    All tools follow a consistent memory_<action> pattern, making the tool set predictable and easy to navigate. The two noun-like names (profile, status) still read as actions in context and do not break the pattern.

    Tool Count5/5

    Eight tools is well-scoped for a memory service, covering retrieval, storage, deletion, inspection, export, and health checks without redundancy. Each tool earns its place.

    Completeness4/5

    The core memory lifecycle is covered: add, search, forget, clear, profile, export, and bootstrap. Minor gaps exist around updating an existing memory in place and importing memories back after export, but these can be worked around.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues