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get_actors_in_level

Lists every actor in the current UE5 level as a JSON array. Returns an error if Unreal is disconnected, so audits don't mistake an unavailable editor for an empty level.

Instructions

Get a list of all actors in the current UE5 level.

Returns a compact single-line JSON array of actor objects when the editor is connected. When Unreal is unavailable, returns a structured JSON error object instead of an empty array so audits do not mistake a disconnected bridge for an empty level. Example: [{"name": "BP_MyActor", "type": "StaticMeshActor"}, ...]

Bug #3 fix:

  • Returns a JSON string so FastMCP sends it verbatim as a single TextContent block (no pydantic_core indent=2 pretty-printing).

  • Connected success responses keep the historical top-level JSON array.

KB: see knowledge_base/10_WORLD_BUILDING.md#overview Example: get_actors_in_level()

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations present, the description carries the full behavioral burden, and it is exemplary: it discloses the connected success format, the disconnected-bridge error object, why errors are preferred over empty arrays, and the JSON-string serialization behavior. This goes well beyond typical descriptions.

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

Conciseness4/5

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

The description is well structured with a front-loaded purpose sentence, a return/behavior section, an example, and a KB reference. It is slightly longer than strictly necessary—the 'Bug #3 fix section is somewhat redundant with the earlier return format explanation—but each part remains informative.

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

Completeness5/5

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

As a zero-argument read-only query, the description is complete: it states what is returned, what happens on success, what happens when the editor is unavailable, includes a concrete JSON example, and points to the relevant KB topic. Nothing an agent needs to invoke it correctly is missing.

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

Parameters4/5

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

The input schema has zero properties and the parameter count is zero, so there are no parameter meanings to clarify. The description adds no parameter semantics, but the zero-parameter baseline is 4 and the schema fully covers the case.

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 first sentence 'Get a list of all actors in the current UE5 level' names a specific verb and resource, and the word 'all' distinguishes it from sibling filtering tools like find_actors_by_class and find_actors_by_name. It is immediately clear what the tool does without opening the schema.

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

Usage Guidelines3/5

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

The description makes the tool's scope clear — all actors, current level, no filtering — which implies when it is appropriate. However, it does not explicitly mention sibling alternatives or state when not to use it, leaving selection guidance to inference.

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

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