Loading ToolBench
Process and validate warranty claims with eligibility checks and fraud detection
Single tool 'process' has a non-verb name ('process' is generic), lacks actionable descriptions, and schema is present but incomplete. Input schema describes nested objects with properties, but lacks enum constraints, bounds, and actionable error recovery guidance. The tool definition is visible in client.py (WarrantyClaimClient.process method), so it is not inferred. However, critical pattern violations and missing descriptions for decision logic reduce score significantly. The output structure is described in docstring but not formally documented as a machine-readable schema. No error handling guidance, no recovery steps, no confirmation patterns for denial decisions. Transport is unknown, which further limits score.
Process a warranty claim. Validates eligibility, scores fraud risk, and generates claim decisions.
Tool name 'process' is generic and violates verb_noun pattern. Does not convey action (e.g., 'evaluate_warranty_claim', 'process_warranty_claim' would be clearer).
Tool description is 121 characters and covers WHAT but not WHEN to use or prerequisites. Lacks recovery guidance for rejection cases.
Input schema lacks enum constraints for 'category' (should enumerate: electronics, appliances, clothing, furniture, toys, beauty, default). Free-form strings invite hallucinated values.
No output schema documented. Return value described in code docstring includes 'decision' enum (approved|denied|review_required), 'fraud_risk' object, 'eligibility' object, 'resolution_options' array. LLM must infer structure from implementation, not from schema.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 33 | 2026-07-28+ | v2 |
No error handling guidance. Tool silently defaults invalid dates to today() (see lines: 'except ValueError: purchase = date.today()'). LLM receives no indication that date parsing failed, no recovery step offered.
Parameter descriptions lack actionable constraints. 'warranty_months (int)' has no bounds (what if -5?); 'price (float)' has no range; 'past_claims (int)' has no range.
No confirmation pattern for destructive decisions (deny/review_required). Tool always executes immediately. Per pattern:confirmation-request, irreversible operations should support dry-run or confirmation.
Customer object is fully optional but fraud detection depends on it. Missing customer data silently defaults to empty dict (cust = customer or {}), which produces high fraud scores on missing customer history. Undocumented dependency.
Date format validation fragile: 'claim_date (YYYY-MM-DD)' stated in description but code silently falls back to today() on parse error. LLM has no way to know parsing failed and may retry with same invalid input.