Three tools with basic schemas and minimal descriptions. All tools have input schemas with proper types (title, description as strings, both required). However, descriptions are generic and under 50 chars, failing the LLM-optimization baseline (target 50-200 chars). No output schemas documented. No error handling guidance. No parameter constraints or enums. Tool names follow verb_noun pattern but lack specificity about what 'suggest' and 'improve' actually return. No indication of idempotency, side effects, or retry safety. Composition is reasonable (three distinct concerns), but descriptions do not explain WHEN to use each tool or what distinguishes them.

• Expand tool descriptions to 80-150 chars. Example: 'suggest_category: Analyzes course title and description to recommend the most appropriate category (e.g., Tech, Business, Design). Returns category name and confidence score. Use when categorizing new courses.'
• Document output schemas explicitly. Add to each tool handler: 'Returns object with fields: suggestedCategory (string), confidence (0-1), reasoning (string)', or similar for tags/improvements.
• Add parameter constraints in descriptions. Example: 'title (string, 1-200 chars): Course title in English or Portuguese. Avoid special characters.' This prevents LLM hallucination of invalid inputs.
• Implement error classification. Distinguish retryable errors (API timeout) from user-fixable (invalid input) from fatal (auth failure). Return structured error with 'error_type' and 'recovery_hint' fields.
• Consider adding tool annotations (readOnlyHint: true for all three, since they do not modify state). This signals to agents that these tools are safe to call speculatively.