Single tool 'analyze_reviews' has a description (good), but the input schema is severely incomplete. The schema visible in the code shows only parameter names and types without proper JSON Schema structure, descriptions for individual parameters, or constraints. The tool accepts 'product_name' (string) and 'reviews' (array), but the array element structure is not formally defined. No output schema is documented. The tool description (58 chars) is below the 194-char baseline and lacks WHEN/WHY guidance. Parameter descriptions are absent entirely, LLMs cannot infer what 'reviews' array elements should contain. The implementation uses hardcoded defaults and keyword matching, which is brittle. No error handling, validation, or recovery guidance. Tool does one thing (analyze), which is good, but the interface is underdeveloped for production use.

• Add formal JSON Schema for 'reviews' array: each element must be an object with 'rating' (integer, 1-5), 'verified' (boolean), 'text' (string, min 10 chars). Document in parameter description.
• Document output schema with all fields: product_name, verdict, total_reviews_analyzed, average_star_rating, verified_purchase_percentage, net_promoter_index, aspect_breakdown (object with aspect names as keys), top_positive_driver, top_friction_factor. Include types and ranges.
• Expand tool description to 150-200 chars: 'Analyzes customer reviews to extract aspect-level sentiment (build quality, performance, battery, value, ease-of-use), compute Net Promoter Score, and identify top drivers and friction points. Use this to understand product strengths and weaknesses from verified and unverified reviews.'
• Fix mcp_server.py tools/call handler to extract and pass product_name and reviews from the request payload to client.analyze_reviews(product_name, reviews).
• Add input validation: reject reviews with missing/invalid fields, return actionable error like 'Invalid review at index 2: missing "text" field. Each review must have rating (1-5), verified (boolean), and text (string).' Enable LLM self-correction.