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
learned-experience
by fitz2882
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TDQS
A3.8/5.0
Scored across 14 tools
Disambiguation4/5
Most tools have distinct purposes: record/inspect/recall form a clear CRUD+query core, while begin_attempt/feedback/reinforce/dismiss handle the verification lifecycle. The main ambiguity is between reinforce and feedback (both report outcomes) and between dismiss and reinforce (both are feedback paths), though the descriptions do clarify the intended use cases.
Naming Consistency3/5
Tool names are mostly single verbs (record, inspect, recall, dismiss, amend, feedback, restore, forget, consolidate, stats, transfer) which is consistent in style, but the mix of single-word verbs and compound names (begin_attempt, maintenance) breaks a strict pattern. There is no verb_noun convention, but the naming is still readable and predictable.
Tool Count4/5
14 tools is within the well-scoped range and each tool addresses a distinct lifecycle concern (recording, querying, verifying, maintaining, transferring). The count feels slightly heavy due to legacy/overlapping feedback tools (reinforce, dismiss, feedback), but it is not excessive.
Completeness4/5
The surface covers the full lesson lifecycle: create (record), read (inspect, recall), update (amend, restore), delete (forget), plus verification (begin_attempt, feedback), maintenance, and transfer. Minor gaps exist—there is no explicit search/list-all tool beyond recall, and the legacy reinforce path creates some redundancy—but the core domain is well covered.
Maintenance
ActivityMaintained
ResponsivenessNo issues