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ue5_verify_behavior

Verify runtime behavior by reading a live actor property and comparing it with an expected value via exact, tolerance, or contains matching, with optional PIE screenshot evidence.

Instructions

Assert runtime behavior: read a live property and compare with the expected value (exact / tolerance / contains), optionally capture a PIE screenshot as evidence. Requires PIE/Simulate running; mismatch is fail-loud. | 行为验证:读运行时属性与期望比对(支持数值容差/子串),可附截图证据;PIE 未运行会 fail-loud。

Input Schema

NameRequiredDescriptionDefault
matchNoexact (default) | contains
worldNopie (default) | editor
captureNoCapture screenshot evidence (default true)
expectedYesExpected value | 期望值
out_pathNoScreenshot relative path (optional)
toleranceNoNumeric tolerance for exact | 数值容差
actor_nameYesActor name in the running world | Actor 名
property_nameYesReflectable property | 可读属性名

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv6.5.4

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and delivers the key traits: the PIE precondition, the fail-loud behavior on both missing PIE and value mismatch, and that a screenshot is captured by default. It stays silent on error semantics for missing actors/properties and on how a successful assertion is reported.

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?

Core behavior and preconditions are front-loaded in the first clause, with supporting detail after. The only waste is the full bilingual duplication of the same content, which roughly doubles length without adding information.

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

Completeness3/5

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

For an 8-parameter tool with no output schema and no annotations, the description covers invocation prerequisites and failure policy but says nothing about the success result shape (pass payload, artifact path) or how property types outside numerics/strings are handled. Adequate but with clear gaps.

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

Parameters3/5

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

Schema description coverage is 100%, so all eight parameters (including match, world, capture, tolerance) are already documented in structured fields. The description only restates the match modes and screenshot option, adding no syntax or interaction detail beyond the schema; baseline 3 applies.

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

Purpose4/5

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

States a specific verb+resource: read a live runtime property and compare it against an expected value, with named match modes and optional screenshot evidence. This clearly distinguishes it from a plain read (ue5_read_pie_property) by the assertion semantics, though no sibling is named explicitly.

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?

Gives a clear precondition (PIE/Simulate must be running) and failure policy (fail-loud on mismatch), which tells the agent when the tool is applicable. It does not explicitly say when to prefer ue5_verify_scenario or ue5_read_pie_property instead, so no exclusions are offered.

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