Encapsulating Thoughts and Intent

Action blocks represent a model’s decision at a given point in its reasoning loop. Rather than producing only a final answer, the model under MCP generates intermediate actions that reflect its plan. Each action block contains structured metadata describing what the model intends to do next. This can include calling a tool, representing clarification, storing an observation, or generating final output.

For example, an agent tasked with summarizing documents might produce this action block:

{
  "type": "tool_use",
  "tool_name": "document_retriever",
  "parameters": {
    "keywords": ["regulatory risk", "vendor compliance"]
  }
}

This tells the execution engine that the model is not ready to respond yet – it wants to gather more information first. The system then perform the tool call (via an API or internal function), captures the result, and feeds it back to the model as a new context object or observation.

Action blocks make reasoning explicit and controllable. The model is not a black box returning mysterious outputs. Each step is visible, interruptible, and reproducible. This is especially important for long-running workflows, regulated environments, or collaborative systems where every decision must be observable and explainable.