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• Provide English translations of all descriptions and parameter annotations. Chinese-only descriptions reduce comprehension for globally-deployed LLMs. Use English as the primary language with Chinese as optional.
• Document output schemas explicitly. Define the structure of 'title, body, tags' for generate_xhs_note and the image prompt format for generate_lifestyle_content as JSON Schema or structured type hints.
• Convert categorical parameters ('profession', 'gender', 'personality', 'mood', 'content_type') to enum types with exhaustive value lists. Remove example-based descriptions and replace with formal constraints.
• Add parameter constraints to descriptions: length limits for 'topic' (e.g. '2-200 characters'), allowed values for enums, and valid ranges for 'age' (e.g. '13-120').
• Implement error handling patterns: document what happens if the API fails, rate-limits, or returns invalid content. Provide retry guidance (retryable vs. fatal).
• Add dependency hints: clarify when to call generate_xhs_note (single topic, simple content) vs. generate_lifestyle_content (persona-driven, emotional tone). Help LLMs choose the right tool.
• Consider splitting generate_lifestyle_content into two tools if the parameter combinations are disjoint (e.g., one for persona-based content, one for scene-based content).
• Add a dry-run or preview parameter to both tools so agents can see drafts before committing to generation (optional but recommended for content tools).