Four tools with complete schemas and Japanese descriptions. Naming follows verb_noun pattern (search_, get_, list_, stats). Schemas are well-formed with proper types and enums. However, parameter descriptions are minimal or absent in several cases, and output schemas are not documented. Error handling is basic. The server is read-only and stateless, which is appropriate for a data feed, but descriptions lack the depth needed for LLM-optimal selection.

• Add outputSchema to each tool definition documenting the response structure. For search_cases: {type: 'object', properties: {total: {type: 'number'}, returned: {type: 'number'}, cases: {type: 'array', items: {type: 'object', properties: {id, company, title, order_type, published_date, provisions, ...}}}}}.
• Expand parameter descriptions with format and constraint details. E.g., 'query: Substring match across company name, product name, and case text. Case-insensitive. Returns up to limit results (default 20, max 100).'
• Add discovery guidance: 'Call stats() first to see available fiscal_year and provision values, then use search_cases() to filter by those dimensions.'
• Implement structured error responses. Instead of {error: '...'}, return {success: false, error: {code: 'NOT_FOUND', message: 'No case with id ...', suggestion: 'Try search_cases() to find cases by company or date.'}}.
• Document the data source and update frequency in tool descriptions. E.g., 'Data sourced from 消費者庁 (Consumer Affairs Agency). Updated [frequency]. All cases include official URL for verification.'