Critical gaps across all dimensions. Tool definitions lack English descriptions (Chinese only), parameter descriptions are minimal or absent, and no input schemas are visible in the provided source code. The two tools (getFavoriteContents, getFavorites) have parameter definitions but no documented output schemas. No error handling guidance, no pagination metadata, and no security considerations evident. Spring AI framework integration is present but tool registration via ToolCallbacks.from() obscures the actual schema definitions, cannot verify schema completeness from source.

• Translate all tool and parameter descriptions to English. Use clear, LLM-optimized language: 'Retrieve a user's Bilibili favorite collections. Returns collection ID, title, and video count. Call this first to discover available collections before fetching contents.'
• Document complete input schemas with JSON Schema: specify types (string, integer), constraints (pageSize: 1 - 40, pageNum: ≥1), and required fields. Make constraints machine-readable via minLength, maxLength, minimum, maximum, enum.
• Add output schema documentation. Specify what fields getFavoriteContents returns (e.g., videoId, title, description, url, thumbnail) and what getFavorites returns (collectionId, title, videoCount). Include example responses.
• Add pagination guidance: 'Results are paginated. Use pageNum and pageSize to iterate. If pageSize=40 and 40 results returned, more data may exist, increment pageNum and retry.'
• Implement error handling with recovery hints: 'User ID not found. Verify the ID is correct or use search_users() to find the user. If the user has no collections, getFavorites returns an empty list.'