---
language:
- en
license: cc-by-4.0
task_categories:
- text-generation
- text-classification
pretty_name: ToolMisuseBench
size_categories:
- 1K<n<10K
tags:
- benchmark
- tool-use
- agent-evaluation
- robustness
- recovery
- deterministic
---

# 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

- Project repository (code + evaluator + baselines):
  https://github.com/akgitrepos/toolmisusebench
- Recommended evaluation flow uses the project CLI and harness.

## 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:

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

Coherence and quality audit reference command:

```bash
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:

```bash
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.

```bibtex
@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}}
}
```