Server has 4 tools with basic schemas and descriptions, but multiple critical quality gaps. All tools start with action verbs (get_, search_) which is good. However, parameter descriptions lack specificity about constraints and ranges. Output schemas are not documented, tools return free-form strings rather than structured objects. Error handling is minimal: most errors are returned as plain strings within the response rather than proper error objects with recovery guidance. No input validation or constraint documentation (e.g., no mention that limit should be >= 1 or <= 100). Parameters like 'type' in search_trending lack enum constraints despite having known valid values ('models', 'datasets', 'spaces'). Descriptions are adequate in length (60-140 chars) but generic and lack the 'when to use this vs that' context LLMs need. No output pagination or limit documentation despite returning lists.

• Convert output from string to structured JSON. Define a schema like {items: [{id: string, downloads: int, likes: int, tags: string[], ...}], total: int, next_cursor?: string}. This enables LLMs to extract and chain data.
• Add enum constraint to search_trending 'type' parameter: {type: 'string', enum: ['models', 'datasets', 'spaces'], description: 'Type of items to search...'}
• Add min/max constraints to all 'limit' parameters: {type: 'integer', minimum: 1, maximum: 100, description: 'Number of items to return (1-100, default: 10)'} and document in description.
• Implement proper error responses with categorization. Return {success: false, error: 'User-fixable description', suggestion: 'Try this instead', retryable: boolean} or use exception handling to separate errors from normal results.
• Add pagination: accept limit (1-100) and optional cursor/offset parameters, and return {items: [...], total_count: int, next_cursor: string | null}. Document in tool description.
• Clarify descriptions with composition hints. E.g., get_trending_models: 'Fetch top trending models by score. Use search_trending to filter by query or topic.' This helps LLMs choose the right tool.
• Document what fields each tool returns. E.g., 'Returns: id (string), downloads (int), likes (int), tags (array of strings), sdk (for spaces only).'
• Add validation inside each tool. If limit < 1 or > 100, return a clear error: 'Invalid limit: {limit}. Must be 1-100.' Do not silently clamp or guess.