All three tools have critical definition gaps. Descriptions are extremely brief (10-50 chars, well below the 194-char baseline for A+ tools). NO parameter descriptions exist, all params lack explanatory text, violating the 100% A+ baseline. Input schemas use Zod but lack formal JSON Schema documentation in the code. No output schemas are documented. Error handling is absent. The 'fetch-weather' tool makes an external API call without timeout, error recovery, or rate limiting. Tool names lack action verbs (e.g., 'calculate-bmi' should be 'get_bmi' or 'compute_bmi'). No tool demonstrates idempotency, pagination, or composition patterns.

• Add 50 - 150 character descriptions to each tool. Example for 'add': 'Compute the sum of two numbers. Returns the result as a numeric value. Use when the user asks to add, sum, or combine numbers.'
• Add descriptions to every parameter. Example: 'a: The first number to add (any real number)' and 'b: The second number to add (any real number)'. For 'fetch-weather', add 'city: The city name (string, e.g., "London"). Returns current weather conditions.'
• Document output schemas explicitly. Example for 'add': 'Returns {content: [{type: "text", text: "<numeric sum>"}]}'. For 'calculate-bmi': 'Returns {content: [{type: "text", text: "<BMI as decimal>"}]}'.
• Add error handling to 'fetch-weather': wrap fetch in try-catch, set a 5-second timeout, return actionable errors like 'Weather API unavailable. Try again in 30 seconds.' or 'City not found. Check spelling and try again.'
• Rename tools to verb_noun pattern: 'add' → 'compute_sum', 'calculate-bmi' → 'get_bmi', 'fetch-weather' → 'get_weather'. This aligns with the 90% baseline of A+ tools and improves LLM intent inference.