Server has 6 tools with basic schemas and descriptions, but significant gaps limit production readiness. All tools have descriptions (10-80 chars, mostly adequate) and input schemas with type definitions. However, output schemas are undocumented, LLMs cannot plan downstream calls or extract results. Parameter descriptions are minimal (e.g., 'Search query' lacks format/constraint guidance). No error handling guidance, no recovery patterns, and no tool annotations (readOnlyHint, destructiveHint). Tool naming follows verb_noun convention (search_, rerank_, health_check, get_) which is good. Composition is reasonable: search_knowledge_base and multi_search_knowledge_base are separate concerns, and search_and_rerank combines common operations. However, the lack of output documentation and error guidance places this in the 'fair' range (60-69 baseline), pulled down by missing schemas and no recovery patterns.

• Document output schemas for all tools. E.g., search_knowledge_base should return {citations: [{id, text, score, source}], total_count, search_time_ms}. This enables LLMs to extract and chain results.
• Add enum constraints to schema for search_type parameter: {type: 'string', enum: ['HYBRID', 'SEMANTIC', 'LEXICAL']}. Remove example values from descriptions; use formal constraints instead.
• Add min/max bounds to numeric parameters: max_results (1-100, default 10), threshold (0.0-1.0, default 0.5), top_k (1-50, default 5). Include these in both schema and description.
• Implement error recovery guidance. E.g., if search fails due to empty query, return 'Query cannot be empty. Provide a search term (e.g., 'machine learning').' If rerank fails due to no results, return 'No results to rerank. Try a broader search query.'
• Add tool annotations to schema: readOnlyHint=true for all tools (they do not modify state). This signals to LLMs that these tools are safe to retry without side effects.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry