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ue_exec_progress

Execute Python code inside a ScopedSlowTask progress dialog to keep the Unreal Editor responsive during long operations. Report progress with _task.enter_progress_frame and display a cancel button.

Instructions

Run a Python snippet inside a ScopedSlowTask with a progress dialog.

Use this for long-running operations (bulk imports, retargeting, etc.) to keep the UE5 editor responsive and show a cancel button.

Your code can call: _task.enter_progress_frame(N, "Step description") to advance the progress bar by N units.

Args: code: Python snippet to execute with progress reporting task_name: Label shown in the Unreal progress dialog total_work: Total work units for the progress bar (default 100)

Returns: JSON string with StructuredResult.

KB: see knowledge_base/32_AGENT_PLAYABLE_SLICE_RECIPE.md#overview Example: ue_exec_progress(code="Example")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
task_nameNoMCP Task
total_workNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

Description explains how to advance the progress bar using enter_progress_frame and mentions a cancel button. This is valuable behavioral context beyond annotations (none provided). However, it doesn't detail error handling or side effects, but the core interaction pattern is well covered.

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?

Concise, well-structured with clear sections for description, usage context, and example. Key information is front-loaded. The example is helpful and keeps it brief.

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?

The tool has a output schema but its structure is not described, and no annotations are provided. The description covers the main usage pattern (progress reporting) and example, but does not explain return value structure or error scenarios. However, for a Python execution tool, this seems sufficient for typical use.

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?

The parameter descriptions in the example are minimal ('Python snippet to execute', 'Label shown in the Unreal progress dialog', 'Total work units'). Schema coverage is 0%, so description must compensate. It adds some meaning but could be more explicit about the format of code and expected behavior of total_work.

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?

Clearly states it runs a Python snippet with a progress dialog, distinguishing it from exec_python which lacks progress UI. The description explains the purpose and context for long-running operations.

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?

Provides when to use: long-running operations like bulk imports. It doesn't explicitly state when not to use or mention alternatives, but the context is clear enough to differentiate from exec_python.

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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