Four tools with basic definitions and schemas present, but significant gaps in parameter descriptions and output documentation. Tool names follow verb_noun convention (convert_temperature, add, get_alerts, get_forecast), which is good. However, parameter descriptions are minimal (e.g., 'Temperature in Celsius' is adequate but lacks context on valid ranges). No output schemas documented, LLMs cannot predict return structure. Error handling is absent; tools return plain strings with no guidance on failure recovery. Resources feature is implemented (greeting resource), but tools lack the structured output and error classification patterns expected in production tools.

• Document output schemas for all tools. E.g., convert_temperature returns {type: 'number', description: 'Temperature in Fahrenheit'}. get_alerts should return {type: 'object', properties: {alerts: {type: 'array', items: {type: 'object', properties: {event, area, severity, ...}}}}}.
• Add enum constraints to parameters. Replace 'US state abbreviation (e.g., CA)' with enum: ['AL', 'AK', ..., 'WY'] or a pattern constraint. This prevents hallucinated state codes.
• Implement error classification and recovery guidance. E.g., if get_alerts fails due to invalid state, return {error: 'Invalid state code', suggestion: 'Valid states are: AL, AK, ...'} instead of a plain string.
• Expand parameter descriptions with ranges and constraints. E.g., 'celsius: Temperature in Celsius (range: -273.15 to 1000, typical: -50 to 50)' helps LLMs avoid invalid inputs.
• Add pagination support to get_alerts and get_forecast. Accept limit (default 20, max 100) and offset parameters. Return {results: [...], total: N, next_offset: ...} to handle large result sets.