This server has critical deficiencies across naming, schema documentation, and parameter design. Both tools have trivial descriptions (under 50 chars), zero input schemas, and names that violate verb-noun conventions. The tools are essentially stubs with TODO comments indicating incomplete implementation. No input validation, no structured output documentation, and no error handling guidance. This is a learning/demo project that should not be used in production.

• Rename tools to follow verb_noun pattern: generate_shopping_list, generate_christmas_wishlist. Action verbs signal intent to LLMs and enable better tool selection.
• Expand descriptions to 100-150 chars. Include WHAT the tool does, WHEN to use it, and what it returns. Example: 'Generate a shopping list based on dietary preferences and budget constraints. Returns a structured list of items with estimated prices. Call this when the user needs help planning grocery purchases.'
• Define input parameters with full schema: type, description, enum constraints, and defaults. Example for shopping list: parameters=['dietary_restrictions' (array of enum), 'budget_range' (object with min/max), 'servings' (integer 1-20, default 4)].
• Document output schema as JSON with typed fields. Instead of returning 'Milk, Eggs, Bread, Butter', return {items: [{name: string, category: string, estimated_price: number}], total_items: int}.
• Add error handling with recovery guidance. Validate all inputs; return actionable errors like 'Invalid budget_range: min must be less than max. Try budget_range={min: 20, max: 100}.'
• Replace TODO stubs with actual implementation. Integrate with real data sources (grocery APIs, preference databases) so tools return meaningful, contextual results.
• Add parameter descriptions explaining format and constraints. E.g., 'dietary_restrictions: Array of dietary restrictions (vegan, gluten-free, kosher, halal). If empty, no restrictions applied.'
• Consider adding optional parameters to customize results: list format (array vs. markdown), include prices (boolean), categories to exclude, etc. This increases utility and gives the agent control.

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

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