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unreal_functional_start

Destructive

Run a single approved AFunctionalTest within an existing standalone PIE session. Provide exact session, world, and revision to execute and poll for pass/fail evidence.

Instructions

Run one approved placed AFunctionalTest in an existing standalone PIE session.

Inspect context and list tests first. Requires exact project/session/world/revision. Trusted test callbacks can change gameplay state. This does not start or stop PIE, run arbitrary functions, retry failures, save maps or certify the whole game. Poll the job for passed/failed/error/timeout/cancelled/interrupted evidence. Cancellation and timeout use the owned test's FinishTest/CleanUp; callbacks are not sandboxed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
test_idYes
expected_stateYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark this destructive, but the description adds crucial context: 'Trusted test callbacks can change gameplay state' and 'callbacks are not sandboxed.' It also clarifies cancellation/timeout semantics via FinishTest/CleanUp, which is valuable behavioral information beyond the annotation flags.

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?

Five compact sentences, each earning its place. The main action is front-loaded, followed by preconditions, limitations, side effects, and follow-up behavior, with no filler or repetition of structural schema details.

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?

Given the destructive nature, the 2-parameter schema, and the presence of an output schema, the description is complete. It covers when to use it, prerequisites, side effects, exclusions, and how to obtain the result. Nothing essential is left for the agent to guess.

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?

Schema description coverage is 0%, so the description must compensate. It maps 'exact project/session/world/revision' directly to the required expected_state fields and implies test_id refers to the AFunctionalTest to run. It does not spell out each field individually, but it provides enough semantic grounding for a 2-parameter 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 a precise verb and resource: 'Run one approved placed AFunctionalTest in an existing standalone PIE session.' It clearly delimits scope with exclusions ('does not start or stop PIE, run arbitrary functions, retry failures...'), distinguishing it from sibling workflow, validation, and job tools.

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?

It gives explicit preconditions ('Inspect context and list tests first', 'Requires exact project/session/world/revision') and explicit when-not guidance ('does not start or stop PIE... save maps or certify the whole game'). It also instructs the agent to poll the job for outcomes, which is an actionable follow-up step.

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