The MCP RAG server provides 5 semantic search and knowledge-base tools with reasonably complete schemas and descriptions. Most tools follow verb_noun naming conventions (rag_search, rag_get_examples, rag_suggest_patterns, rag_validate_approach, rag_get_stats) and have parameter descriptions. However, several critical gaps reduce quality: (1) output schemas are completely undocumented, the source code shows parameter inputs but no indication of what these tools return, forcing LLMs to guess at response structure; (2) error handling guidance is absent, tools provide no recovery hints for common failures; (3) descriptions, while present, are moderately detailed but lack the precision needed for optimal LLM tool selection (e.g., 'Validate if a proposed implementation approach follows documented best practices' does not specify what 'documented best practices' means or what failure looks like); (4) one tool (rag_get_stats) has an empty input schema {}, which is acceptable but not ideal for discoverability. Parameter constraints are strong in most tools (enums for categories, complexity, domain, language), and parameter descriptions are specific. No security issues detected (read-only operations, no secrets exposed). Overall, this is solidly above-average in parameter definition but significantly hampered by missing output documentation and weak error handling guidance.

• Document output schemas for all 5 tools. For rag_search, specify: returns an array of objects with fields like 'id', 'title', 'content', 'relevance_score', 'category', 'url'. For rag_get_examples, return array of {language, code, description, complexity, topic}. For rag_suggest_patterns, return {pattern_name, description, applicability, code_example}. For rag_validate_approach, return {is_valid: boolean, issues: string[], suggestions: string[]}. For rag_get_stats, return {documents: number, files: number, categories: string[], last_indexed: ISO8601}.
• Add error handling guidance to each tool description. Example for rag_search: 'If no results match the query, try a simpler query or adjust the threshold parameter (default 0.1). If the database is not indexed, call rag_get_stats to check status.'
• Enhance tool descriptions with concrete examples of what 'semantically' means. Replace 'Search project documentation and code examples semantically' with 'Perform vector-similarity search across documentation to find relevant concepts, not just keyword matches. E.g., querying "element styling" returns results for CSS and attributes, not just literal matches.'
• For rag_suggest_patterns serviceType parameter, clarify when it applies: 'Only provide if suggesting Azure service patterns; omit for canvas/UI domains. Example values: "Microsoft.Web/sites", "Microsoft.Storage/storageAccounts".'
• Add pagination support to rag_search and rag_get_examples if results can exceed the limit parameter (default 5/20). Return total_count and next_offset/cursor so agents can iterate through large result sets.