Single tool 'analyze_reviews' has a verb-noun name (good), but critical gaps undermine quality. Tool description is present (72 chars) but lacks WHEN to use it and what it returns. Input schema is visible but incomplete: 'reviews' parameter has no type annotation in the schema definition shown (array items lack proper JSON Schema typing). Parameter descriptions exist but are minimal (10-15 chars each). No output schema documented, LLMs cannot plan downstream calls or extract structured data. No error handling guidance. The tool accepts default parameters silently, masking missing input. Overall, this reads as a proof-of-concept rather than production-grade tooling.

• Add a complete output schema documenting all response fields: product_name (string), verdict (enum: STRONGLY_RECOMMENDED|RECOMMENDED_WITH_MINOR_RESERVATIONS), total_reviews_analyzed (integer), average_star_rating (number 0-5), verified_purchase_percentage (string), net_promoter_index (number -100 to +100), aspect_breakdown (object with keys: build_quality, performance, battery_life, value_for_money, ease_of_use; values: number 0-10), top_positive_driver (string), top_friction_factor (string).
• Expand tool description to 150-250 chars: 'Analyzes customer reviews to extract aspect-level sentiment (build quality, performance, battery life, value, ease of use), compute Net Promoter Score, and generate a product recommendation verdict. Use this to synthesize customer feedback before making purchase or product improvement decisions.'
• Add validation and error handling: reject empty reviews array with message 'reviews array cannot be empty. Provide at least 1 review object with rating (1-5), verified (boolean), and text (string) fields.' Return structured error responses, not silent defaults.
• Enhance parameter descriptions: 'reviews: Array of review objects. Each object MUST have: rating (integer, 1-5 stars), verified (boolean, true if purchase verified), text (string, review content 10-500 chars). Minimum 1 review required.'
• Add per-aspect confidence scores or citation counts to aspect_breakdown so LLMs understand which aspects are well-supported vs. inferred from few mentions.