A
licenseNot graded
qualityA
maintenanceEnables AI agents to persist and recall episodic memories across sessions, consolidating experiences into reusable rules and lessons to reduce repeated mistakes and improve task performance.
1
MIT
memshelf-mcp
by ignatenkofi
Related Servers
Alternatives to memshelf-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityAmaintenanceProvides persistent, searchable memory for AI agents, enabling them to retain, recall, and reflect on information across conversations.1919 PyPI1MIT
- AlicenseNot gradedqualityFmaintenanceEnables persistent, portable memory for AI agents across sessions, devices, and providers with token-efficient 5-level lazy loading and automatic session capture.10 npmMIT
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to automatically capture durable knowledge and retrieve only relevant, token-bounded context from a secure local-first long-term memory, with support for progressive disclosure, snapshots, health diagnostics, and background tasks.-
Memra MCP Serverofficial
AlicenseNot gradedqualityCmaintenanceProvides persistent memory for AI agents with hybrid semantic and lexical recall, including write-time contradiction detection and token-budgeted retrieval.MIT- AlicenseNot gradedqualityBmaintenanceEnables persistent, graph-based memory for AI agents, allowing them to store, traverse, and recall relationships between facts, decisions, and context across sessions for efficient reasoning and reduced token usage.MIT
TDQS
A4/5.0
Scored across 14 tools
Disambiguation5/5
Each tool targets a clearly distinct operation within the memory-shelf lifecycle: reading (index, recall, search), writing (shelve, import), diagnostics (lint_digest, doctor), maintenance (rebuild, rollup, purge, resolve), and insights (stats, advise). Potential overlaps like lint_digest vs. doctor or rebuild vs. resolve are explicitly differentiated by scope and timing in the descriptions.
Naming Consistency5/5
All 14 tools consistently use the memshelf_ prefix and lower snake_case. The action/artifact naming is predictable across the set, even when a few names are noun-like (stats, doctor) rather than purely verb_noun.
Tool Count5/5
With 14 tools, the server sits in the well-scoped 3-15 range and covers a full lifecycle without obvious filler. Each tool maps to a distinct phase or concern of managing a memory shelf.
Completeness4/5
The surface covers initialization, creation, import, retrieval, search, metrics, validation, rebuild, rollup, retention purge, conflict resolution, and diagnosis. Minor gaps remain, such as no direct per-episode edit, manual delete, or export operation, though retention and rollup mitigate deletion and archival needs.
Maintenance
ActivityActive
ResponsivenessSlow