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Scored across 5 tools
Most tools are distinct: extract_entities and get_metadata are clearly unique, while rag_query and semantic_search both relate to searching content but serve different purposes (finding information vs. generating answers). cag_context is about building context for generation, so it is distinct. Some ambiguity exists between rag_query and semantic_search, but descriptions clarify their roles.
All names use snake_case, but they are not consistently verb_noun. extract_entities and get_metadata follow the verb_noun pattern, while rag_query, cag_context, and semantic_search are noun phrases with abbreviations (rag, cag). This mixes conventions and makes the names less predictable.
Five tools is well-suited for a specialized knowledge base server, covering query, context, extraction, search, and metadata. Each tool serves a distinct function and none feel superfluous or missing.
The toolset covers the main operations for interacting with a knowledge base: querying (rag_query), searching (semantic_search), building context (cag_context), extracting entities (extract_entities), and retrieving metadata (get_metadata). It lacks tools for ingesting or managing content, but for an LLM-facing toolset, the core workflows are complete.