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    • A
      license
      A
      quality
      C
      maintenance
      Enables coding agents to share a persistent local memory store across sessions and clients, saving, searching, updating, and retrieving facts, decisions, incidents, preferences, references, and snippets with semantic and lexical search.
      10
      MIT
    • F
      license
      Not graded
      quality
      A
      maintenance
      Provides AI coding agents with a local-first persistent memory layer they can read and write during sessions, storing decisions, preferences, error patterns, and research per project or globally. Enables semantic and keyword search, formatted context recall, and cross-project guideline surfacing without API keys or cloud storage.
      -
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI coding agents to share a persistent, local-first memory of architectural decisions, conventions, bug root causes, and session handoffs, searchable via full-text ranking, so context carries across tools and sessions without any cloud dependency.
      MIT
    • F
      license
      Not graded
      quality
      A
      maintenance
      Gives AI coding agents persistent, local-first memory so they can store, semantically search, and recall decisions, preferences, error patterns, and cross-project guidelines directly during a session. It also optionally queues tasks for autonomous execution against that same memory.
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to read and write persistent, local-first memory across sessions and projects, with semantic search, global guidelines, and optional autonomous task execution.
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to store and retrieve persistent project and cross-project memories via semantic search, so decisions, preferences, error patterns, and guidelines survive across sessions. Optionally supports autonomous task execution built on the same local-first knowledge store.
      -

    TDQS

    A4.1/5.0

    Scored across 23 tools

    Disambiguation5/5

    Each tool has a clearly distinct role: reads (search, resolve, traverse, aggregate, history), writes (prepare/commit), batch operations, and import. Although legacy direct-write tools overlap with the prepare/commit workflow, they are explicitly marked as legacy and direct agents to the new flow, eliminating ambiguity.

    Naming Consistency4/5

    Most tools follow a consistent verb_noun pattern (create_record, search_records, get_record_history), but a few break the pattern with noun-first names like batch_read and batch_resolve_records, and aggregate stands alone as a bare verb. Minor deviation, still readable.

    Tool Count4/5

    23 tools is on the higher side, but the scope is broad: collections, records, links, two-phase commits, batch operations, import, search, and audit. Six legacy tools duplicate the prepare/commit surface, adding weight, but the core tool set is still well-scoped for the domain.

    Completeness5/5

    The surface covers the full lifecycle: collection discovery and creation, record create/read/update/archive, linking/unlinking, resolution, search, aggregation, graph traversal, history, and atomic batch/import operations. The only apparent absence is permanent deletion, which seems intentional in an audit-focused system.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues