ToolMind is a large-scale, high-quality tool-agentic dataset with 160,000 synthetic data instances generated using over 20,000 tools and 200,000 augmented open-source data instances. Its data synthesis pipeline constructs a function graph based on parameter correlations and uses a multi-agent framework to simulate realistic user–assistant–tool interactions. The dataset was created by mlx-community and was last updated on June 6, 2026.
Use Cases
- Training language models for tool selection based on described function graphs and parameter correlations.
- Benchmarking agent performance on realistic user-assistant-tool interaction sequences.
- Developing multi-agent systems that simulate tool-use workflows.
- Studying the impact of synthetic data augmentation on tool-use reasoning tasks.
Strengths
- Contains 160,000 synthetic data instances.
- Integrates over 20,000 distinct tools.
- Includes 200,000 augmented open-source data instances.
- Employs a multi-agent framework for realistic interaction simulation.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Data may reflect bias inherent to the synthesis methods and source data.
Provenance
- Source
- mlx-community on Hugging Face.
- Collection Method
- Synthetic data generation pipeline using a multi-agent framework.
- Freshness
- Last updated 2026-06-06 08:34:45; freshness should be verified.