Two tools with basic definitions and simple parameter schemas. Both tools have non-empty descriptions and typed input parameters, meeting minimal requirements. However, descriptions are generic, output schemas are not documented, error handling is minimal, and no parameter constraints are enforced. Tool names follow verb_noun convention (get_*) appropriately. The implementation uses mock data and basic string lookup, production-ready error messages and recovery guidance are absent.

• Add formal output schema declarations to both tools. Define return type with typed fields (e.g., tier: string, status: enum, id: string) and document them in tool metadata.
• Enhance parameter descriptions with constraints: 'Customer email address (valid email format, e.g. [email protected], max 254 chars)' and 'Customer ID (alphanumeric, format CUST_NNN, 8 chars)'.
• Expand tool descriptions to 100-200 characters with WHEN/WHY context: 'Use to check if a customer has an active subscription and what tier they're on. Returns tier name (Free/Pro/Enterprise) and account status. Call this before fetch_usage or get_contract_sla to qualify the customer.'
• Implement proper error handling with categorized responses: return {"error": "Customer not found", "type": "user_fixable", "suggestion": "Try searching by name with search_customers() first"} for not-found, vs {"error": "Service unavailable", "type": "retryable"} for transient failures.
• Add input validation: reject invalid email formats, reject customer IDs not matching pattern CUST_*, and return 400 with constraint violation details rather than 'not found'.
• Convert response format from text blobs to structured objects: return {"tier": "Enterprise", "status": "Active", "customer_id": "CUST_001"} directly, not {"content": [{"type": "text", "text": "..."}]}.
• Add parameter typing hints in responses to enable downstream tool chaining: when get_customer_tier succeeds, ensure customer_id is returned so the agent can call get_contract_sla without an extra lookup.