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is_play_in_editor_active

Read-onlyIdempotent

Check if the game is running in Unreal's Play In Editor to confirm play status and avoid save issues.

Instructions

THE oracle for "is the game running right now": asks ULevelEditorSubsystem::IsInPlayInEditor and returns is_pie_active plus status 'Playing'/'Stopped' and the current level name. Cheap, read-only, no params. Use it to confirm start_play_in_editor / stop_play_in_editor (both answer from before the async transition finishes) and as the gate before any save -- saving with PIE up is refused by the engine. WARNING: Do NOT use the pie_active field of list_dirty_packages for this: it can report false with PIE running and answering.

Returns: { is_pie_active: bool, current_level: string (the EDITOR's current level, not the PIE world), status: 'Playing'|'Stopped' }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds real value beyond them: it flags the tool as cheap with no params, explains the async timing caveat that makes it the authoritative check, notes the engine refuses saves while PIE is up, and adds the non-obvious caveat that current_level is the editor's level rather than the PIE world.

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?

Front-loaded with the core purpose ('THE oracle for is the game running right now'), then usage, warning, and returns in a tight sequence. Each sentence carries distinct information: purpose, timing rationale, save gate, anti-pattern, and return fields.

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?

No output schema exists, but the description fully specifies the return object (is_pie_active, current_level with its caveat, status enum). For a zero-param read-only predicate with full annotations, nothing an agent needs to call 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 tool takes zero parameters, so the baseline is 4. The description goes further by documenting the return shape and annotating the current_level semantics, though there are no input params whose meaning needs clarifying.

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?

States a specific verb and resource ('is the game running right now') and names the exact underlying API (IsInPlayInEditor) and return fields. It explicitly distinguishes itself from start_play_in_editor / stop_play_in_editor and from list_dirty_packages, so an agent can select it without opening a schema.

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

Usage Guidelines5/5

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

Gives explicit when-to-use cases (confirming start/stop PIE since those answer before the async transition completes; gating before any save) and an explicit when-not-to-use warning against the pie_active field of list_dirty_packages, with the reason it misfires. Nothing is left 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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