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pie_capture_log

Capture the tail of the current Unreal Engine project log to verify recent output. Filter by text and optionally save the captured lines as an artifact for later review.

Instructions

Capture the tail of the current Unreal project log for verification.

Args: max_lines: Maximum number of log lines to return contains: Optional case-insensitive filter save_artifact: Save captured lines to .mcp_artifacts/logs artifact_name: Artifact filename stem when saving

Returns: JSON string with log file path, captured lines, and optional artifact path.

KB: see knowledge_base/32_AGENT_PLAYABLE_SLICE_RECIPE.md#overview Example: pie_capture_log()

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
containsNo
max_linesNo
artifact_nameNopie_log
save_artifactNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden. It does disclose the return format and the side effect that save_artifact writes to `.mcp_artifacts/logs`. However, it does not mention prerequisites such as whether a PIE session must be running, failure modes, or whether repeated calls overwrite artifacts.

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?

The description is compact and front-loaded: a one-sentence purpose, terse Args/Returns blocks, a KB reference, and a minimal example. Every line adds value and there is no filler or repetition of schema types.

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?

Given four optional parameters, an output schema, a KB reference, and an example, the description is nearly complete for correct invocation. The main missing piece is usage context relative to sibling logging/PIE tools, but the calling contract itself is well specified.

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

Parameters5/5

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

The schema has 0% description coverage, but the Args section fully compensates for all four parameters: max_lines limits output, contains is a case-insensitive filter, save_artifact controls file saving, and artifact_name specifies the filename stem. This is exactly the semantic enrichment the schema lacks.

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?

The description opens with a specific verb and resource: 'Capture the tail of the current Unreal project log for verification.' It clearly communicates what the tool does and includes a KB pointer to the playable-slice recipe. However, it does not explicitly differentiate this from sibling tools like get_recent_output_log.

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 phrase 'for verification' and the KB reference imply the intended context, but there is no explicit statement of when to use this tool versus alternatives, no prerequisites, and no exclusions noted. It is more than no guidance but less than clear contextual routing.

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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