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get_scene_insights

Parses Godot scene files to extract node hierarchy, attached scripts, and signal connections into structured JSON, enabling quick understanding of scene architecture without reading raw .tscn files.

Instructions

Understand a Godot scene file (.tscn): node hierarchy, attached scripts, and signal connections. Returns structured JSON so the agent can understand scene architecture without reading raw scene files.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scenePathYesPath to the scene file (relative to project)
projectPathYesPath to the Godot project directory
includePropertiesNoInclude node properties in the structure (default: true)
includeConnectionsNoInclude signal connections in the structure (default: true)
includeScriptInsightsNoInclude analysis for scripts attached to nodes (default: true)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of disclosing side effects and behavioral traits. It mentions that structured JSON is returned, but never explicitly states that the tool is read-only/non-mutating, what happens on invalid paths, or whether it parses only vs. modifies. The 'get' name hints at safety but the description leaves this implicit.

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?

Two tight sentences, with the core action and target file type front-loaded and the output format stated immediately. Every clause adds information: file type, extracted content, return type, and intended benefit.

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?

With no annotations and no output schema, the description must carry more context. It conveys the high-level return content but does not describe the JSON shape, required path semantics beyond schema names, behavior for missing/invalid scenes, or side-effect safety. It is a viable minimal description, not a complete one.

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 the schema already documents all five parameters. The description adds no parameter-specific meaning beyond naming the three insight categories that map to the boolean flags, which is exactly the baseline value expected when the schema carries the detail.

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 action ('Understand a Godot scene file') with concrete content areas: node hierarchy, attached scripts, and signal connections, and distinguishes the tool's scope from raw file reading. It does not explicitly name or differentiate sibling tools like get_node_insights, so it falls just short of a 5.

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?

The description gives a clear context: use this when the agent needs structural scene understanding 'without reading raw scene files'. It does not mention alternatives or exclusions, but the intended scenario is evident enough for typical selection.

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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