Skip to main content
Glama

spawn_actor

Spawn actors into the loaded level by providing a class path and optional position, rotation, scale, and label.

Instructions

Spawn an actor into the loaded level.

Args: class_path: Object path of the class to spawn. Either a native class (e.g. "/Script/Engine.StaticMeshActor") or a Blueprint class asset path (e.g. "/Game/Devices/BP_MyDevice.BP_MyDevice"). Use list_content_assets or execute_python to discover available Fortnite device/prop Blueprint paths. location: World location {"x", "y", "z"} in cm. Defaults to origin. rotation: World rotation {"pitch", "yaw", "roll"} in degrees. scale: Actor scale {"x", "y", "z"}. Defaults to (1, 1, 1). label: Optional editor display name (shown in the World Outliner).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelNo
scaleNo
locationNo
rotationNo
class_pathYes
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description must carry the burden of behavioral disclosure. It reveals defaults, units (cm, degrees), and the fact that spawning occurs into the loaded level. However, it does not cover failure behavior, return values, or persistent side effects, leaving some ambiguity for an agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a one-sentence summary followed by a clean Args block. Every sentence contributes value, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 5 parameters, no output schema, and no annotations, the description does a good job covering all parameters and providing usage context for discovering class paths. However, it omits details about return values and error handling, which would be useful given the lack of an output schema. Overall, it is fairly complete but not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully compensates by explaining each parameter in detail: class_path format with examples, location/rotation/scale key names and units, defaults, and label semantics. This adds substantial meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Spawn an actor into the loaded level', which is a specific verb+resource statement. It clearly distinguishes this tool from sibling tools like delete_actor and duplicate_actor by focusing on creation from a class path.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool (to create an actor) and even directs users to `list_content_assets` or `execute_python` for discovering valid class paths. However, it does not explicitly contrast with alternative tools like duplicate_actor or state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dylannalex/uefn-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server