Three tools with complete schemas and reasonable descriptions, but significant gaps in parameter documentation and error handling guidance. All tools are read-only (good for safety), but descriptions lack actionable context for LLM selection. Parameter descriptions exist but are minimal (avg ~40 chars vs. baseline 72). No output schema documentation visible. No error recovery guidance. Naming is clear and verb-based (memory_search, memory_recall, memory_context), following conventions well.

• Document output schemas for all three tools. Specify: memory_search returns [{id, title, snippet, tool, date, score}]; memory_recall returns [{id, category, content, date, project}]; memory_context returns {project_meta, docs: {identity, memory, plan, learning, note}, recent_conversations: [{id, date, title}]}.
• Formalize memory_recall.category as a JSON Schema enum: ['conversation', 'memory', 'identity', 'plan', 'config', 'learning', 'skill', 'note']. Add similar enums for memory_search.tool_filter.
• Expand parameter descriptions with format and constraint guidance. E.g., memory_search.query: 'Natural language search query (10-500 chars). Examples: "how to handle async errors", "project X architecture decisions". Broader queries return more results; specific queries are more precise.'
• Add error recovery hints to tool descriptions. E.g., memory_search: 'If no results, try a shorter or more general query. If results are irrelevant, use tool_filter to narrow by source tool.'
• Remove references to memory_conversation and memory_blueprint from memory_context description, or add them as actual tools. Dangling references break the composition model.