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:

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}}
}