Tool evaluation method compares utility cost and access constraints
Counterfactual Tool Ranking under Utility, Cost, and Privilege Constraints
Machine Learning
Summary
Evaluating different tools fairly is tricky because it involves understanding their usefulness, cost, and who can use them. The authors studied eleven tools inspired by real business needs and tested ways to rank them accurately using different methods. Their tests showed that some evaluation methods work better in certain situations, especially when environments change. They also found ways to handle cases where certain tools lack data support and proposed a way to identify improvements in such challenging scenarios. Their main contribution is a clearer, testable way to evaluate tools rather than introducing new tool estimators or claiming production readiness.
What this means in practice
- •For enterprise data teams: Rank and select enterprise tools more reliably by accounting for cost, utility, and access restrictions using the paper's falsifiable evaluation methods.
- •For ai platform engineers: Evaluate and compare AI models or policies under changing environments and incomplete support by applying the paper's approach to control bias in ranking.
Authors
Jiapeng Li
Abstract
Counterfactual tool evaluation must distinguish authority, historical support, and what a comparison actually estimates. We study these distinctions with eleven executable enterprise-inspired tools, exact-propensity logs, and real local Model Context Protocol transport. An initial 45-run synthetic study is retained, then challenged by 30 realized-return control runs and 15 experiments on 1,930 independently released Berkeley Function Calling Leaderboard (BFCL) tasks. Full-return direct regression reverses an initially favorable doubly robust (DR) evaluation result in the linear setting: mean absolute errors are 0.0139 for direct regression and 0.0272 for DR. Under a shifted environment, DR retains an advantage, with errors 0.0227 versus 0.0948. On function-name-group-disjoint BFCL-derived splits, direct and DR selectors obtain balanced accuracies of 81.85% and 79.83%. Two pinned local Qwen2.5 models are evaluated on the same 200 held-out tasks, exposing a strong failure to abstain under the fixed prompt. We further characterize policy differences under missing support: unsupported actions shared by two policies cancel, allowing point identification of an incremental change when neither absolute value is identifiable. A disagreement-preserving fallback achieves this property in all five support-gap runs, but conservative sampling bounds do not certify deployment improvement. The contribution is a falsifiable evaluation method and independent public evidence, not a new DR estimator, official BFCL leaderboard score, or production-agent safety claim.