Datasets for building agents that use tools: function-calling and tool-calling examples, multi-step agent trajectories, and interactions with real APIs and Model Context Protocol (MCP) servers. The training and evaluation data behind systems that retrieve, reason, and act rather than simply answer.
5sources
The de facto benchmark for how well language models call functions, APIs and tools. Built by UC Berkeley's Gorilla project, it spans Python, Java, JavaScript and REST with simple, parallel, irrelevance-detection, multi-turn and agentic cases. Apache 2.0 and freely available.
A widely used open dataset of about 113,000 synthetic multi-turn chat conversations that include function calls and their results, made by Glaive AI. One of the most downloaded open datasets for fine-tuning models to call tools, released under Apache 2.0.
An open instruction-tuning dataset for teaching general tool use to language models, built for the ToolLLM project over 16,464 real-world REST APIs from RapidAPI across 49 categories, with single-tool and multi-tool, multi-step solution paths.
The largest open tool-agentic dataset: over 1.5 million trajectories synthesised from 495 real-world MCP servers spanning 2,000 plus tools, with multi-turn, sequential and parallel tool calls backed by real executions and error handling. Built by Agent-Ark and released under Apache 2.0, it is premier open data for training retrieval-and-tool (MCP) agents.
Salesforce's APIGen-generated dataset of 60,000 verified function-calling examples spanning 3,673 executable APIs, each checked through format, execution and semantic stages. The training data behind the xLAM action models and core capability data for tool-using agents.