AI App MCP
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TDQS
Scored across 5 tools
All tools have distinct roles: search and get_document are clearly separate (search vs. reading full doc), build_rag_prompt and normalize_user_query serve different pipeline stages, and health_check is standalone. Slight potential confusion exists between search_knowledge_base and get_document, but descriptions resolve this.
All tool names follow a consistent verb_noun snake_case pattern: search_knowledge_base, get_document, build_rag_prompt, normalize_user_query, health_check. The pattern is predictable and readable.
Five tools is a well-scoped count for a RAG-focused server. Each tool serves a clear purpose in the pipeline, and the count feels neither too thin nor overly heavy.
The set covers the core RAG flow (normalize, search, retrieve, build prompt) plus health check. Missing operations like adding/updating knowledge base documents or a combined retrieve tool, but these are minor gaps for the apparent scope.