Three tools with minimal quality. All have descriptions (positive), but descriptions are extremely brief (10-20 chars), violating the 10-1024 char guideline and falling below the 20-char floor for description scoring. Input schemas are present but lack depth: parameters have types and minimal descriptions. No output schemas documented. No error handling guidance. Tool names lack clear verb-noun structure (e.g., 'questionSearch' should be 'search_questions'). No parameter constraints, enums, or validation hints. No evidence of idempotency, pagination, or composition patterns. This is a minimal proof-of-concept implementation, not production-ready.

• Expand tool descriptions to 50-200 chars. Example: 'questionSearch' → 'Search interview questions by keyword. Returns matching questions with difficulty, tags, and content. Use when user asks about technical interview topics.' (current: ~27 chars)
• Rename tools to verb_noun snake_case: questionSearch → search_questions, getWeather → get_weather, calculate → add_numbers. This matches MCP convention and helps LLMs parse intent from names alone.
• Document output schemas for each tool. Specify fields, types, and whether results are paginated. Example for questionSearch: {questions: [{id, title, difficulty, tags, content}], total, hasMore}
• Add parameter constraints and format hints. Example for getWeather city param: 'City name (2-50 chars, e.g. Beijing, Shanghai). If not found, returns available cities.' Enums for difficulty in questionSearch if applicable.
• Add error handling guidance. Example: 'If no results found, try broader search terms or check spelling. If API timeout, retry after 5 seconds.' Classify errors as retryable vs user-fixable.

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

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