Server has 4 tools with explicit @Tool annotations and Spring AI integration. All tools have descriptions (10-200 chars, within baseline range). Input schemas are visible with typed parameters and descriptions. However, output schemas are not documented in the source, responses are custom POJOs (KnowledgeSearchResponse, RuleResponse, etc.) with no visible schema definitions. Parameter descriptions are present but some lack constraints (e.g., topK accepts 1-10 but no enum/min/max validation visible). Error handling is basic, IllegalArgumentException for missing rules, but no recovery guidance. Tool names follow verb_noun pattern (search_, get_, review_) which is good. No security issues detected (read-only operations, no secrets in params). Composition is reasonable, each tool has a single responsibility, though search_knowledge and review_commerce_design overlap in intent (both search rules).

• Document output schemas for all 4 tools. Include field names, types, and descriptions. Example: 'Returns {query: string, resultCount: int, matches: [{ruleId: string, title: string, matchConfidence: float, ...}], ...'
• Add @Min(1) @Max(10) annotations to topK and maxFindings parameters, or use @Pattern regex. Validate in method body and return actionable error: 'topK must be 1-10, got: ' + topK.
• Convert 'detail' parameter to an enum: @ToolParam(...) ResponseDetail detail with values SUMMARY, FULL. Prevents hallucinated values.
• Enhance error handling in get_rule: catch IllegalArgumentException and return structured error with recovery hint: 'Rule not found. Try search_knowledge(query="...") to find related rules, or list available rules with get_checklist(domain="...").'
• Clarify tool intent in descriptions. Add to search_knowledge: 'Use when you have a natural-language question.' Add to review_commerce_design: 'Use when you have a design artifact (API, DDL, code) to review.' This disambiguates selection.