Remembra
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- AlicenseAqualityDmaintenanceProvides persistent memory for AI assistants via MCP, enabling them to store and recall facts, preferences, and tasks across conversations using either local file storage or a cloud backend with semantic search.55 npmMIT
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TDQS
Scored across 13 tools
Most tools are clearly distinct: store, get, update, forget, archive, revive, search, list, relate, history, and maintain each map to a separate operation. The main ambiguity is memory_batch, which wraps store/update/delete/export and could be confused with the individual write/update/delete tools, though its batch purpose is stated.
The memory_ prefix is consistent and most suffixes are action verbs: store, search, list, forget, get, relate, update, archive, revive, maintain. memory_batch and memory_history break the verb pattern, but the overall convention remains predictable and readable.
Thirteen tools is well within the ideal range and each tool covers a distinct aspect of memory management: CRUD, search, versioning, relationships, archival, batch operations, ingestion, and maintenance. No tool feels redundant or unnecessary.
The surface fully covers memory lifecycle: store, retrieve, list, search, update, delete, archive, revive, version history, relation management, batch processing, and automated digesting from transcripts. There are no obvious dead ends or missing core operations for the stated purpose.