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This paper introduces ToolSense, a diagnostic framework designed to audit the parametric tool knowledge of large language models (LLMs) by generating three distinct benchmarks that evaluate tool retrieval capabilities under varying levels of query ambiguity. The study reveals a significant knowledge-retrieval dissociation, where LLMs exhibit a dramatic drop in performance on realistic retrieval tasks compared to fully-specified benchmarks, indicating that high retrieval accuracy does not necessarily correlate with true understanding of tool semantics. By providing an open-source framework and benchmarks, this work highlights critical limitations in current retrieval approaches and encourages further exploration of LLM capabilities in real-world applications.
ToolSense uncovers a surprising 50-64% drop in retrieval performance for LLMs when faced with realistic, ambiguous queries, challenging assumptions about their tool understanding.
Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck. As embedding-based retrieval approaches rely on compact encoders that may under-capture specialized tool semantics, parametric tool retrieval addresses this by encoding each tool as a virtual token appended to the LLM vocabulary, fine-tuned in two stages (memorization then retrieval SFT) to use the LLM as a retriever, achieving strong performance on standard ToolBench retrieval benchmarks. Yet these benchmarks use verbose, fully-specified queries, and their evaluation applies constrained decoding that restricts outputs to valid token paths, neither reveals whether the model actually understands its tools. We introduce \textbf{ToolSense}, an open-source LLM-powered diagnostic framework that takes any tool catalog as input and automatically generates three benchmarks: a Realistic Retrieval Benchmark (RRB) with queries at three ambiguity tiers, an MCQ probing benchmark, and a QA probing benchmark. Applying ToolSense to ToolBench (~47k tools) and evaluating five parametric model training configurations reveals a knowledge-retrieval dissociation: on RRB queries, several configurations collapse by ~50-64 percentage points compared to fully-specified ToolBench benchmarks, falling below the embedding-model baseline. Additionally, despite strong retrieval performance, some models score near-random on factual probes, suggesting a knowledge-retrieval dissociation. We open-source the ToolSense framework and the ToolBench diagnostic benchmarks at https://github.com/SAP/toolsense.
