| name | golem-annotate-agent-scala |
| description | Adding prompt and description annotations to Scala agent methods. Use when the user asks to add descriptions, prompts, or documentation metadata to agent methods for AI/LLM discovery. |
Annotating Agent Methods (Scala)
Overview
Golem agents can annotate methods with @prompt and @description annotations. These provide metadata for AI/LLM tool discovery — agents with annotated methods can be used as tools by LLM-based systems.
Annotations
@prompt("...") — A short instruction telling an LLM when to call this method@description("...") — A longer explanation of what the method does, its parameters, and return value
Usage
import golem.runtime.annotations.{agentDefinition, description, prompt}
import golem.BaseAgent
import scala.concurrent.Future
@agentDefinition(mount = "/inventory/{warehouseId}")
trait InventoryAgent extends BaseAgent {
class Id(val warehouseId: String)
@prompt("Look up the current stock level for a product")
@description("Returns the number of units in stock for the given product SKU. Returns 0 if the product is not found.")
def checkStock(sku: String): Future[Int]
@prompt("Add units of a product to inventory")
@description("Increases the stock count for the given SKU by the specified amount. Returns the new total.")
def restock(sku: String, quantity: Int): Future[Int]
@prompt("Remove units of a product from inventory")
@description("Decreases the stock count for the given SKU. Returns a Left if insufficient stock.")
def pick(sku: String, quantity: Int): Future[Either[String, Int]]
}
Guidelines
@prompt should be a natural-language instruction an LLM can match against a user request@description should document behavior, edge cases, and expected inputs/outputs- Both annotations are optional — omit them for internal methods not intended for LLM discovery
- Annotations have no effect on runtime behavior; they are purely metadata
