Two well-defined tools with strong schema validation and clear descriptions. Both tools have complete JSON Schema with type constraints, patterns, and ranges. Descriptions are concise (40-60 chars) and action-oriented. Input validation is strict (customer_id pattern, amount bounds, reason minLength). Error handling distinguishes protocol errors (validation) from domain errors (business logic). However, output schema is not formally documented, responses are returned as structuredContent but the shape is inferred from code rather than declared. Tool composition is minimal (only 2 tools), limiting assessment of chaining patterns. No tool annotations (readOnlyHint/destructiveHint) despite clear risk levels.

• Add outputSchema to both tool definitions documenting the response structure. For get_customer_record: {type: object, properties: {customer_id, name, email, refundable_balance_usd, ...}, required: [...]}. For trigger_refund: {type: object, properties: {refund_id, customer_id, amount, reason, status, remaining_refundable_usd}, required: [...]}.
• Add tool annotations to schema: get_customer_record should include 'readOnlyHint: true' in inputSchema; trigger_refund should include 'destructiveHint: true' to signal irreversibility.
• Enhance error messages with recovery guidance. E.g., 'Refund of $X exceeds refundable balance of $Y. Call get_customer_record to verify balance, or reduce amount and retry.'
• Clarify reason parameter constraint in plain language: 'Explanation for the refund (minimum 10 non-whitespace characters, e.g., "Customer reported defective product")', then remove the regex from description since JSON Schema pattern is machine-readable.
• Document the refund_id format in trigger_refund description: 'Returns a refund ID in format RFND-XXXXXXXXXX for tracking.' This helps LLMs understand the response structure.

Inferred effective spec: 2025-06-18+.

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