Six tools with basic schemas and minimal descriptions. All tools have input schemas with typed parameters and descriptions, meeting baseline requirements. However, descriptions are extremely terse (10-40 chars), well below the 50-200 char LLM-optimized range. No output schemas documented. No error handling guidance. Math tools lack context on when to use divide vs multiply. Weather tools are mock implementations with no real API integration details. No tool annotations (readOnlyHint, destructiveHint, idempotentHint). Naming is acceptable (verb_noun pattern) but descriptions fail to explain WHEN to use each tool or what happens on error.

• Expand all descriptions to 50-150 chars. Example: 'Add two integers and return the sum. Use when the user asks for addition or total. Returns an integer.' This tells LLM what, when, and what to expect.
• Document output schemas for each tool. E.g., divide returns {type: 'number', description: 'The quotient as a float. Returns error if denominator is zero.'}
• Add error recovery hints to divide: 'If denominator is zero, ask the user to provide a non-zero value or use a different operation.'
• Disclose that weather tools return mock data. Update descriptions: 'Returns simulated weather data (not real-time). For demo purposes only.'
• Add tool annotations to schema: readOnlyHint=true for all tools, idempotentHint=true for math operations (same inputs always produce same output).

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

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