This server has three tools with mixed quality. All tool names follow verb-noun conventions (query_*, list_*, get_*), which is good. However, critical issues undermine the overall score: (1) Input schemas lack type definitions for some parameters and descriptions are present but generic; (2) Output schemas are not explicitly documented in the code, returns are inferred from docstrings; (3) Error handling is minimal, errors are returned as dict keys but lack structured categorization or recovery guidance; (4) Parameter descriptions exist but are brief and lack constraints (e.g., 'limit' has no min/max bounds); (5) The 'question' parameter in query_snowflake_gold accepts free-form text with no enum/validation constraints, risking hallucinated queries. Tool definitions appear to be directly in agent/core.py (not inferred), so per-tool caps don't apply, but the implementation quality is below production baseline.

• Add explicit output schema documentation to each tool. E.g., query_snowflake_gold returns {'success': bool, 'view_name': str, 'sql_query': str, 'data': list[dict], 'row_count': int, 'question': str, 'error'?: str}. Update docstrings to include 'Returns' section with field types.
• Add min/max bounds to numeric parameters. Change 'limit' input schema to include 'minimum': 1, 'maximum': 100 (or MAX_QUERY_LIMIT from config). Update parameter description to state: 'Maximum rows to return (1 - 100, default 10)'.
• Declare supported question types as an enum hint or constraint. Either: (a) add a 'question_type' enum parameter (daily_sales, customer_segment, product_performance) and accept 'question' as clarification, OR (b) document in the description: 'Supported patterns: daily sales, revenue, customer type, customer segment, product, popular, top selling, best. Questions not matching these patterns return an error with suggestions.'
• Restructure error responses to follow recovery-guide pattern. Return {'success': false, 'error': str, 'error_type': 'unsupported_question|database_error|timeout', 'suggested_action': str}. E.g., 'error_type': 'unsupported_question', 'suggested_action': 'Try asking about: daily sales, customer types, or top products. Or call list_available_data() to see supported question patterns.'
• Enrich list_available_data and get_data_help return values. Instead of returning generic dicts, structure them: list_available_data returns {'views': [{'name': str, 'description': str}], 'supported_question_types': [str]}. Document in docstring.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry