Programmatic tool calling, where models write Python stubs instead of JSON tool calls, matches or beats JSON tool calling on BFCL v4 in 11 of 14 models, but the gains depend on benchmark design and model generation.
HyperTool: Beyond Step-Wise Tool Calls for Tool-Augmented Agents
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abstract
Tool-augmented LLM agents commonly rely on step-wise atomic tool calls, where each invocation, observation, and value transfer is exposed in the main reasoning trace. This creates an \emph{execution-granularity mismatch}: locally deterministic tool workflows are unfolded into repeated model-visible decisions, consuming context and forcing the model to manage low-level dataflow in the trace. We introduce \textbf{HyperTool}, a unified executable MCP-style tool interface that changes the model-visible unit of tool execution. A model invokes HyperTool with a code block that can call existing tools through their original schemas, manipulate returned values, and pass intermediate results locally, folding deterministic tool subroutines into a single outer call. To train models to use this interface, we synthesize HyperTool-format trajectories from cross-tool compositional tasks and verify them in real MCP environments. On MCP-Universe, HyperTool improves average accuracy from 15.69\% to 35.29\% on Qwen3-32B and from 9.93\% to 33.33\% on Qwen3-8B, and surpass GPT-OSS and Kimi-k2.5 on average accuracy, showing that our HyperTool can substantially improve multi-step tool use.
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cs.CL 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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The Bitter Lesson of Tool Calling
Programmatic tool calling, where models write Python stubs instead of JSON tool calls, matches or beats JSON tool calling on BFCL v4 in 11 of 14 models, but the gains depend on benchmark design and model generation.