Three tools with basic verb-noun naming (get_*) and minimal descriptions. All tools have input schemas with type definitions and parameter descriptions, meeting baseline requirements. However, descriptions are extremely terse (10-30 chars), falling well below the 194-char production baseline and the 50-200 char LLM-optimized range. No output schemas documented. Error handling is generic (ValueError wrapping). No parameter constraints (enums, ranges, patterns). Missing guidance on when to use each tool vs. alternatives. No idempotency hints or chaining metadata in responses.

• Expand tool descriptions to 100-200 chars. Example: 'get_story: Fetch a single HackerNews story by ID. Returns title, author, score, URL, and comments. Use after get_top_stories to retrieve full details for a story of interest.'
• Document output schemas explicitly. Add docstrings like 'Returns: {stories: [int], total: int}' or use Pydantic models with schema export.
• Add parameter constraints: limit (1-100, default 10), story_id (positive integer), user_id (non-empty string). Use JSON Schema minItems, maxItems, pattern, enum.
• Implement structured error responses: {error: string, code: string, recovery: string}. E.g., 'error: "Story not found", code: "NOT_FOUND", recovery: "Verify story_id is valid; call get_top_stories to list available stories."'
• Standardize response field names across tools. Use consistent keys like 'data', 'id', 'type' to reduce LLM reasoning overhead.