The server provides 6 tools with reasonable descriptions and clear names. However, there are significant gaps in schema completeness, parameter descriptions, and error handling guidance. The server lacks input validation details, enum constraints for multi-valued parameters, and actionable error recovery guidance. Most tools have functional descriptions but fall short of LLM-optimized clarity. The predict_churn tool has an exceptionally detailed parameter list (19 params) but lacks enum constraints for the many yes/no and categorical fields, inviting hallucinated values. Several tools return raw strings instead of structured output, which wastes tokens and complicates downstream parsing.

• Convert all categorical parameter enums. In predict_churn schema, declare gender as enum: {"type": "string", "enum": ["Male", "Female"], "description": "..."}. Apply to contract, internet_service, payment_method, and all yes/no fields.
• Refactor run_pandas_query to remove example values from description. Replace 'Example queries: "SeniorCitizen == \'Yes\'..."' with 'Use pandas query syntax with valid column names from get_dataset_info(). Operators: ==, !=, >, <, <=, >=, and, or. Example: tenure > 12 and contract == \'Two year\''
• Add limit and offset parameters to run_pandas_query with defaults (limit=10, offset=0). Document the parameter descriptions: 'limit: Maximum number of rows to return (1-100, default 10).' Change return to {"total_count": int, "returned_count": int, "offset": int, "limit": int, "rows": [...]}'
• Convert tool responses to structured JSON. Replace get_summary_stats' df.describe().to_string() with a JSON object: {"column": "tenure", "count": 7043, "mean": 32.4, "std_dev": 24.6, "min": 0, "25%": 9, "50%": 29, "75%": 55, "max": 72}. Use similar structure for predict_churn.
• Document output schema in tool descriptions. Add to each tool's description: 'Returns: {...}' block with field types. Example for get_summary_stats: 'Returns a JSON object with keys: column (string), count (number), mean (number), std_dev (number), min (number), 25% (number), 50% (number), 75% (number), max (number).'
• Refactor predict_churn to accept optional customer_id parameter. If provided, auto-populate fields from df[df['CustomerID']==customer_id]. Reduce required params to only those not in dataset. Add description: 'If customer_id is provided, missing parameters are auto-filled from the dataset. Otherwise, all 19 parameters are required.'