The RAG MCP server has clear, well-structured tool definitions with consistent verb-noun naming. All 7 tools are explicitly registered with descriptions and input schemas. However, several definition quality gaps reduce the score below 70: (1) Most parameter descriptions lack constraint details (ranges, formats, enums). (2) Output schemas are inferred from response model classes but not fully documented in the descriptions themselves. (3) Error handling descriptions are absent, tools do not guide LLMs on recovery paths. (4) Some parameter descriptions are generic or incomplete. For example, 'metadata' in embed_document lacks detail on expected structure. The 'ask_question' tool signature shows inconsistency: the code expects a QuestionRequest object but the tool parameter definition shows flattened individual params. Overall, the server demonstrates solid foundational quality (naming, basic schemas) but lacks the depth and precision expected of production-grade tools (50-200 char descriptions with constraints, explicit error guidance, fully documented output contracts).

• Add explicit numeric constraints to all parameters. Example: 'top_k: integer, range 1 - 100, default 5' and 'min_similarity: number, range 0.0 - 1.0, default 0.4'. Use parameter descriptions to state these constraints in prose, as JSON Schema 'minimum'/'maximum' are not visible to LLMs.
• Expand error guidance in tool descriptions. For each tool, document failure modes: 'If file not found, returns error with status 'file_not_found'. Try embed_document() first to load the document.' This guides LLM recovery.
• Document output schemas in tool descriptions. Example: 'Returns SearchDocumentsResponse with fields: results (array of {filename, chunks, similarity_score}), message (string), total_count (integer).' LLMs cannot reason about downstream calls without this.
• Implement pagination for list_documents() and search_documents(). Add optional 'limit' (1 - 100, default 20) and 'offset' parameters. Return 'total_count' and 'has_more' in responses. This prevents context window exhaustion on large databases.
• Add confirmation pattern to delete_document(). Offer a 'dry_run' boolean parameter or a separate 'preview_delete_document()' tool that shows what will be deleted before confirming. This prevents accidental data loss.
• Clarify metadata parameter in embed_document(). Example: 'metadata: optional object with custom key-value pairs (e.g. {"source": "wiki", "author": "jane"}). All values must be strings or numbers.'
• Fix ask_question signature mismatch. Either flatten the parameters in the implementation or document the QuestionRequest object structure in the tool description.

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

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