Datasets:

cesun
/

When2Tool

configs:
  - config_name: single_hop
    default: true
    data_files:
      - split: train
        path: single_hop/train-*.parquet
      - split: test
        path: single_hop/test-*.parquet
  - config_name: multi_hop
    data_files:
      - split: train
        path: multi_hop/train-*.parquet
      - split: test
        path: multi_hop/test-*.parquet
license: mit
task_categories:
  - question-answering
language:
  - en
tags:
  - tool-use
  - agents
  - llm
  - benchmark
pretty_name: When2Tool
size_categories:
  - 1K<n<10K

When2Tool

Benchmark dataset for "LLM Agents Already Know When to Call Tools — Even Without Reasoning" (arXiv:2605.09252).

Overview

When2Tool is a benchmark of 18 environments designed to study when LLM agents should call tools. Tasks range from trivially solvable without tools to impossible without them, across three categories of tool necessity:

Computational Scale (5 envs): Calculator, Statistics, Counting, Matrix, Prime
Knowledge Boundaries (5 envs): Retriever, Historical Year, Game Rule, Hash, Decoding
Execution Reliability (5 envs): List Manipulation, DateTime, Code Executor, Schedule, Regex Match
Multi-hop (3 envs): Calculator, Retriever, Code Executor (3-step chains)

Each environment has three difficulty levels (easy, medium, hard) that create a clear decision boundary between tool-necessary and tool-unnecessary tasks.

Dataset Structure

Configs

single_hop: 15 single-hop environments (900 train / 2,250 test)
multi_hop: 3 multi-hop environments with 3-step chains (180 train / 450 test)

Fields

Field	Type	Description
`id`	int	Unique task identifier
`difficulty`	str	`easy`, `medium`, or `hard`
`multi_step`	bool	Whether the task requires multiple tool calls
`instruction`	str	The task instruction given to the agent
`env_name`	str	Environment name (e.g., `CalculatorEnv`)
`tools`	str (JSON)	Available tools for this environment
`parameters`	str (JSON)	Environment parameters (e.g., corpus for retriever)
`answer`	str	Expected final answer
`steps`	str (JSON)	Intermediate steps for multi-hop tasks
`tags`	str (JSON)	Environment and task type tags

Loading

from datasets import load_dataset

# Single-hop tasks
ds = load_dataset("Trustworthy-ML-Lab/When2Tool", "single_hop")

# Multi-hop tasks
ds_mh = load_dataset("Trustworthy-ML-Lab/When2Tool", "multi_hop")

# Access a sample
print(ds["test"][0]["instruction"])
print(ds["test"][0]["env_name"])

Citation

@article{sun2026when2tool,
  title={LLM Agents Already Know When to Call Tools -- Even Without Reasoning},
  author={Sun, Chung-En and Liu, Linbo and Yan, Ge and Wang, Zimo and Weng, Tsui-Wei},
  journal={arXiv preprint arXiv:2605.09252},
  year={2026},
  url={https://arxiv.org/abs/2605.09252}
}