The LLM & agent era
How did chat models grow into agents that use tools?
Chat models can only describe doing something. A line of research taught them to call external tools and act on what comes back, the step that turned chatbots into agents.
A chat model can describe how to look something up, but it cannot actually look it up. It also struggles with tasks that smaller, simpler programs handle easily, like exact arithmetic or fetching a current fact.[2] The fix was to let the model call external tools and use what comes back. That step is what turned chatbots into agents.
Two 2022 and 2023 papers mark the shift. ReAct showed that a model could interleave reasoning with actions: it thinks about a step, takes an action such as querying a Wikipedia interface, reads the result, and reasons again. Grounding its answers in real lookups cut down on confident wrong answers.[1] Toolformer went a step further and trained a model to teach itself when to call tools, including a calculator, search engines, a translation system, and a calendar, deciding which to use and what to pass to it.[2]
Once models could use tools, the loose end was wiring. Each tool needed its own custom connector, so adding many tools to many apps meant a tangle of one-off integrations. In November 2024, Anthropic introduced the Model Context Protocol (MCP), an open standard for how a model and an outside tool or data source talk to each other, so a connector built once can be reused.[3] That is the world we are in now: models that not only answer but act, through a shared way of plugging tools in.
The tool-use standard
- Model Context Protocol (MCP) ↗
Open standard from Anthropic for connecting models to external tools and data sources through one reusable interface.
References
- ReAct: Synergizing Reasoning and Acting in Language Models — arXiv (Yao et al.)
- Toolformer: Language Models Can Teach Themselves to Use Tools — arXiv (Schick et al., Meta AI)
- Model Context Protocol — Wikipedia