Server has well-structured tool definitions with complete input schemas and clear descriptions. All 6 tools are explicitly registered with proper JSON Schema. Tool names follow verb_noun convention (store_, retrieve_, get_, link_, summarize_, explore_). Descriptions are substantive (100-200 chars) and explain purpose clearly. However, output schemas are entirely undocumented, LLMs cannot predict response structure. Parameter descriptions are present but lack constraints (enums, ranges, formats). Error handling is not visible in the code. Tool annotations (readOnlyHint/destructiveHint) are correctly applied. No pagination guidance for list-returning tools. Security: source_url is auto-injected server-side for store_observation, which is good practice.

• Document output schema for each tool. Example for retrieve_relevant: 'Returns {observations: [{id, content, source_url, timestamp, tags, similarity_score}], total_count, has_more}'. This enables LLMs to chain results correctly.
• Add JSON Schema constraints to numeric parameters: limit with minimum:1, maximum:20; depth with minimum:1, maximum:4; confidence with minimum:0, maximum:1. This prevents invalid values at the source.
• Add error handling examples to descriptions. E.g., retrieve_relevant: 'If no results match, try broadening the query or removing tag filters. If semantic search fails, fall back to summarize_context with topic_filter.'
• Clarify the distinction between retrieve_relevant (semantic similarity), get_working_memory (semantic + recency blend), and summarize_context (time/topic filter). Add guidance: 'Use retrieve_relevant for general knowledge lookup; get_working_memory for current task context; summarize_context for historical review.'
• Add pagination support: document that limit caps results, and if more exist, return a next_cursor or indicate total_count. Offer guidance: 'If limit=5 and total_count=50, call again with cursor to fetch next batch.'