The server defines 2 RAG tools with complete JSON Schema input definitions and descriptions in Japanese. However, significant gaps reduce quality: (1) schemas lack output documentation, no return type or result structure is documented; (2) parameter descriptions are minimal (avg ~40 chars) and many parameters lack context about dependencies and valid ranges; (3) no error recovery guidance, handlers return bare error text without actionable next steps; (4) required parameter logic is inconsistent (search requires 'query' only, yet handlers fail if 'user_id' or 'notebook_id' are missing, contradicting the schema); (5) no tool annotations (readOnlyHint, destructiveHint) despite both being read-only operations; (6) descriptions are in Japanese, reducing LLM utility for English-dominant clients. Both tools are explicitly registered with names and schemas (not inferred), so full per-tool scoring applies. Average of per-tool scores: (58 + 46) / 2 = 52.

• Add complete output schema documentation for both tools. Example for 'search': { 'type': 'object', 'properties': { 'results': { 'type': 'array', 'items': { 'type': 'object', 'properties': { 'document_id': {...}, 'chunk_id': {...}, 'content': {...}, 'score': {...} } }, 'total_count': { 'type': 'integer' }, 'is_error': { 'type': 'boolean' } } }
• Rewrite descriptions in English (or provide bilingual English/Japanese). Minimum 50-100 chars; include purpose, use case, and when to call. Example: 'search: Performs semantic/vector search across indexed documents scoped to a user and notebook. Use this to find relevant chunks matching a natural language query. Returns ranked results with similarity scores. Requires user_id and notebook_id.'
• Fix required parameter arrays. For 'search': set required: ['query', 'user_id', 'notebook_id']. For 'get_document_count': set required: ['user_id', 'notebook_id']. Ensure schema matches code validation.
• Add numeric constraints: limit (integer, minimum: 1, maximum: 100, default: 5), context_size (integer, minimum: 0, maximum: 10, default: 1). Add enum/pattern validation for tenant, notebook, user_id, notebook_id if possible.
• Enrich error messages with recovery guidance. Example: 'user_id is required. Call list_users() first if you don't have a user_id, or provide it as a parameter.'
• Add readOnlyHint: true to both tools in the schema (when MCP protocol allows tool annotations). Both read from the database and do not modify state.
• Add parameter dependency documentation in descriptions. Example: 'with_context (boolean): If true, also retrieve context chunks before/after the main result. Requires context_size parameter. Default: true.'

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

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