MemoryD demonstrates strong semantic design with 17 well-named, verb-prefixed tools (memory_remember, memory_recall, memory_forget, etc.). All tools have descriptions (avg ~100 chars, within baseline 34-392 range). Input schemas are present and properly typed with JSON Schema. However, parameter descriptions are sparse or missing entirely, most params lack explanatory text beyond the name. Output schemas are not documented. Error handling is minimal (generic 400/404 responses). The domain is sophisticated (memory consolidation, belief derivation, provenance tracking) but the tool interface underexplains itself to LLMs.

• Add descriptions to every parameter. For 'query', explain: 'Natural language search term to find relevant memories (lexical, importance, confidence, recency scoring).' For 'limit', explain: 'Max results to return (1-100, default 10).' For 'kind', explain: 'Filter by memory type: decision, state, procedural, semantic, or speculation.'
• Document output schemas for all tools. E.g., memory_recall returns {results: [{id, content, kind, confidence, importance, created_at, updated_at, metadata}]}. memory_beliefs returns {beliefs: [{statement, confidence, evidence_ids, subject, key}], conflicts: [...]}. This enables LLMs to plan downstream calls.
• Enhance error messages with recovery guidance. Instead of 'error: not found', return 'Memory ID xyz not found. Try memory_recall(query=...) to search, or memory_timeline() to browse recent memories.'
• Add enum constraint to 'relation' parameter in memory_link. Define valid relation types (e.g., 'supports', 'contradicts', 'refines', 'supersedes', 'related_to') and document each.
• Implement dry-run or confirmation for destructive operations. Add optional 'confirm=true' param to memory_forget and memory_merge. Return a preview of what will be deleted/merged and require explicit confirmation before executing.