sigdelakshey/ToolMisuseBench · Datasets at Hugging Face

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ToolMisuseBench

ToolMisuseBench is a deterministic, offline benchmark dataset for evaluating tool-using agents under realistic failure conditions, including schema misuse, execution failures, interface drift, and recovery under budget constraints.

This dataset is intended for reproducible evaluation of agent tool-use behavior, not for training a general-purpose language model.

Dataset Summary

ToolMisuseBench evaluates whether an agent can:

  • make valid tool calls under schema constraints
  • recover after failures (timeouts, rate limits, authz, drift, adversarial errors)
  • satisfy task goals under bounded tool-call/step/retry budgets
  • minimize policy violations and invalid tool invocations

All tasks are synthetic and generated with deterministic seeds to ensure reproducibility.

Repository and Evaluator

Supported Evaluation Use Cases

  • baseline benchmarking for tool-using agents
  • robustness testing under controlled tool failures
  • recovery-quality analysis after failure injection
  • budgeted success tradeoff analysis (success vs tool-call cap)

Data Structure

Dataset layout:

  • train/tasks.jsonl
  • dev/tasks.jsonl
  • test_public/tasks.jsonl
  • manifest.json
  • v0_1_freeze.json

Each row in tasks.jsonl is a single benchmark task containing:

  • task_id
  • split (train | dev | test_public)
  • difficulty (easy | medium | hard)
  • domain (crud | retrieval | files | scheduling | mixed)
  • instruction
  • toolset_id
  • tool_schemas
  • initial_state
  • success_criteria
  • budget (max_steps, max_tool_calls, max_retries, timeout_ms)
  • fault_plan
  • gold_summary (optional)
  • seed

Dataset Size (v0.1 Release)

  • Train: 5000
  • Dev: 800
  • Test Public: 1000
  • Total: 6800

Domains

  • CRUD
  • Retrieval
  • Files
  • Scheduling

Fault Model

Supported fault types:

  • schema_drift
  • rate_limit
  • timeout
  • authz
  • adversarial_error

Faults are declaratively specified per task and replayed deterministically.

Viewer Note on Null Values

In the Hugging Face table viewer, nested fields inside fault_plan.trigger and fault_plan.payload may appear as null for some rows.

This is expected: different fault types use different subsets of fields, and the viewer displays a unified schema across all rows. A null value in this context typically means "not applicable for this fault type," not missing or corrupted data.

Data Generation

Generated synthetically using deterministic templates, seeded randomization, and task-level coherence checks.

Generation reference command:

toolmisusebench generate \
  --version v0.1 \
  --out data/toolmisusebench_v0_1 \
  --seed 42 \
  --size-profile large

Coherence and quality audit reference command:

python -m generator.quality_report \
  --dataset data/toolmisusebench_v0_1 \
  --splits train,dev,test_public

Scoring and Evaluation

Use the official evaluator in the project repo.

Example:

toolmisusebench eval \
  --dataset data/toolmisusebench_v0_1 \
  --split test_public \
  --agent heuristic \
  --report out/report.json

For detailed metric definitions, see SCORING.md in this dataset repository.

Reproducibility Notes

  • deterministic generation and replay under fixed seeds
  • per-task fault plans are deterministic
  • checksums included in manifest.json
  • freeze metadata included in v0_1_freeze.json

Limitations

  • synthetic tasks do not capture all real-world API/tool semantics
  • benchmark is focused on controlled robustness comparisons, not full production realism

Ethics and Privacy

  • no personal data
  • no proprietary user logs
  • no sensitive external data sources used

License

Dataset: CC-BY-4.0 Code/evaluator: MIT (see project repository)

Citation

If you use ToolMisuseBench, please cite the project.

@misc{toolmisusebench2026,
  title={ToolMisuseBench: A Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systems},
  author={ToolMisuseBench Authors},
  year={2026},
  howpublished={\url{https://github.com/akgitrepos/toolmisusebench}}
}
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