Memory MCP
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- AlicenseNot gradedqualityAmaintenanceProvides persistent cognitive memory for AI coding agents, enabling them to recall architecture decisions, coding preferences, and project facts across sessions.68MIT
- AlicenseNot gradedqualityBmaintenanceProvides persistent, searchable memory and knowledge capture for AI-assisted development, enabling agents to retain decisions, bugs, and patterns across sessions and projects.MIT
- FlicenseNot gradedqualityAmaintenanceGives AI coding agents a local-first persistent memory they can read and write directly during sessions, storing decisions, preferences, error patterns, and guidelines in per-project and global SQLite stores. Enables semantic and keyword retrieval of relevant context across sessions so agents start work already knowing a codebase's conventions and history.-
- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents to persist and retrieve durable project knowledge across coding sessions, capturing decisions, conventions, lessons and reusable skills through ordinary chat requests. It supports searching, editing and reviewing saved items, and promoting local notes into Git-shareable versioned knowledge alongside repository changes.70 npmMIT
- FlicenseNot gradedqualityAmaintenanceEnables 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.-
- AlicenseAqualityAmaintenanceProvides long-term memory for AI coding agents, enabling facts like stack, conventions, and decisions to be recorded once and recalled across sessions.8344 npmMIT
TDQS
Scored across 10 tools
Most tools have clear, distinct purposes: brief loads the full context, recall searches broadly, gotchas/conventions retrieve specific types, and gotcha/convention/learn/prefer add different kinds of knowledge. The overlap between recall and the focused retrieval tools is intentional and described, but the singular/plural pairs (convention vs. conventions, gotcha vs. gotchas) could cause misselection if the agent does not read descriptions carefully.
Tool names follow no consistent pattern. Some are verbs (brief, recall, learn, prefer, fix), some are plural nouns used for retrieval (gotchas, conventions), some are singular nouns used for adding (gotcha, convention), and one is a compound (memory_bootstrap). This mix of conventions makes the naming unpredictable.
With 10 tools, the set is well-scoped for a memory/knowledge server. Each tool covers a necessary function: adding knowledge (four types), retrieving it (three approaches), fixing entries, and bootstrapping a project. The count feels appropriate and not bloated.
The tool surface covers the full lifecycle of memory: create (gotcha, convention, learn, prefer), read (brief, recall, gotchas, conventions), update/delete (fix), and initialization (memory_bootstrap). The only minor gap is that there is no dedicated list tool for 'learn' or 'prefer' entries, but recall and brief surface them adequately.