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Related Servers

Alternatives to scoped_memory

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Provides durable project context for coding agents, including project maps, session history, and explicit memories, all stored locally.
      46 npm
      7
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables MCP-compatible coding agents to persist and retrieve durable project context—such as decisions, preferences, issues, tasks, and notes—locally across sessions without cloud services.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Provides persistent project memory for AI coding agents, enabling context retention across sessions via event logging, briefing generation, and querying.
      MIT
    • F
      license
      Not graded
      quality
      C
      maintenance
      Local-first deterministic project memory for AI coding agents, with context packs, decisions, gates, risks, scoped claims and explicit checkpoints in project-owned files.
      -
    • A
      license
      Not graded
      quality
      A
      maintenance
      Provides AI agents with persistent, local, and shareable project memory by storing decisions and code context in a searchable SQLite index, supporting keyword and semantic search via MCP.
      39 PyPI
      3
      MIT

    TDQS

    B3.3/5.0

    Scored across 8 tools

    Disambiguation4/5

    Each tool has a distinct responsibility: ingestion, recall, forgetting, status, checkpointing, and project init. However, memory_recall and memory_engineering_context both produce context packets, and memory_remember overlaps somewhat with memory_ingest_project and memory_checkpoint, requiring careful reading of descriptions.

    Naming Consistency3/5

    All tools share the memory_ prefix, but the pattern after the prefix is inconsistent: verb_noun forms like memory_ingest_project and memory_init_project, bare verbs like memory_forget and memory_remember, and nouns like memory_engineering_context, memory_status, and memory_checkpoint. This is readable but not a single predictable convention.

    Tool Count5/5

    Eight tools is well-scoped for a memory server covering project initialization, ingestion, remembering, checkpointing, recall, forgetting, and status. Each tool has a clear place in the workflow, and none feels redundant.

    Completeness4/5

    The memory lifecycle is largely covered: init, ingest, remember, checkpoint, recall, forget, and status. Minor gaps include no direct single-memory retrieval or search operation, and the relationship between memory_engineering_context and memory_recall is not fully clarified.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues