Memory System for Cursor using MCP - Provides persistent context awareness for Claude
Server has 8 tools with complete JSON schemas and descriptions, but quality is inconsistent. Tool names follow verb_noun convention (save_, retrieve_, update_, delete_, list_, search_, get_, clear_). Descriptions are present (avg ~100 chars) but lack actionable context for LLM selection. Parameters have types and descriptions, but many lack constraints (enums, ranges, patterns). No output schemas documented. Error handling is minimal, no recovery guidance or categorization. Destructive operations (delete_memory, clear_memories) lack confirmation patterns. Vector embedding logic is present but undocumented in tool descriptions.
Clear all memories from the database
Delete a memory entry from the database
Get statistics about stored memories
List all memories with optional filtering and pagination
Retrieve memories from the database with optional semantic search using vector embeddings
Save a memory entry to the database with optional vector embedding for semantic search
Search memories using semantic similarity with vector embeddings
Destructive operations lack confirmation or dry-run patterns. delete_memory and clear_memories can permanently erase data without reversibility. clear_memories has a 'confirm' boolean but no guidance on recovery.
No output schemas documented. Tool descriptions do not specify what fields are returned, forcing LLMs to infer structure. This breaks tool chaining and increases hallucination risk.
Numeric parameters lack constraints. 'limit' and 'offset' have no min/max bounds; 'threshold' (0-1) is documented but 'limit' is not. Unbounded numbers invite absurd values.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 48 | 2026-07-28+ | v2 |
Update an existing memory entry in the database
Error handling is absent. No recovery guidance, categorization (retryable vs fatal), or actionable error messages. LLMs cannot self-correct on failures.
Vector embedding implementation is internal and undocumented in tool descriptions. 'embedding' parameter in save_memory and 'semantic_search' in retrieve_memory/search_memories lack explanation of what embeddings do or when to use them.