The server defines 3 tools with basic structure, but has significant gaps in descriptions, parameter documentation, and error handling. Tool names are clear and follow verb_noun convention (get_*, all starting with appropriate action verbs). However, parameter descriptions are minimal or missing in the schema, output schemas are undocumented, and error handling lacks recovery guidance. The server is functional for simple read-only operations but falls short of production quality standards.

• Document the output schema for each tool. For get_total_revenue, specify it returns {"total_revenue": float}. For get_sales_by_product, document the structure of each record (e.g., {"Product": str, "Units Sold": int, "Price": float, "Total Revenue": float}). For get_top_selling_products, clarify it returns {"product_name": revenue_float, ...}.
• Expand tool descriptions to 100-200 characters with explicit WHEN-to-use guidance. Example: 'Get total revenue across all products. Call this for high-level sales summaries. For breakdown by product, use get_sales_by_product().'
• Add type and description to all input schema parameters. Verify get_top_selling_products 'n' parameter has a 'description' field in the JSON schema, not just in the docstring.
• Implement pagination or result limits. Cap get_sales_by_product to return at most 50 records and document the limit. Add a 'limit' parameter to get_top_selling_products with a range of 1-100 and default of 5.
• Enhance error messages with recovery hints. When product_name is not found, return: {"error": "Product 'X' not found.", "hint": "Call get_top_selling_products() to see available products."} to guide the LLM to the next step.
• Add tool annotations. Mark all three tools with readOnlyHint=true since they only read data and have no side effects.
• Specify the fields expected in responses. In docstrings or schema, list the exact keys/fields the response will contain (e.g., 'Response: {"total_revenue": float}').