Check whether Unreal Python exposes required PCG classes for Ghost, returning a support status report.
Report whether Unreal Python exposes the PCG classes needed by Ghost.
KB: see knowledge_base/10_WORLD_BUILDING.md#4-procedural-content-generation-pcg Example: pcg_check_support()
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It communicates that this is a read-only support query ('Report whether') and references KB documentation, but it does not disclose session requirements (e.g., whether Unreal must be running) or failure behavior. Adequate for a simple check, but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three compact elements: a one-sentence purpose, a KB pointer, and an example call. No filler or repetition, and the purpose is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With zero parameters and an output schema present, the description covers the core invocation need; the KB anchor and example round it out for a low-complexity tool. The main omission is guidance on when to run this check relative to other PCG operations, which prevents a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters at 100% coverage, so there is nothing for the description to add. The included example call pcg_check_support() confirms the no-argument invocation, matching the baseline for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description opens with a specific action—'Report whether'—and names the exact resource: Unreal Python's exposure of PCG classes needed by Ghost. This clearly distinguishes it from sibling PCG tools like pcg_create_graph_asset and pcg_create_volume, which mutate assets rather than probe support.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use, when-not-to-use, or alternative conditions are stated. The intended use is only implied by the tool name and 'Report whether...' wording, and the KB link suggests broader workflow context but does not explain when to prefer this check over other PCG tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.