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beckettlab

Beckett — MCP for Godot

by beckettlab

get_remote_tree

Read-only

Get a scoped snapshot of the running game's live scene tree, using path, depth, and node limits to stay within token budgets. Collapse repeated leaf siblings to keep the output compact.

Instructions

Dump the live scene tree of the RUNNING game (runtime counterpart of get_scene_tree). SCOPE IT to stay under token limits — a full game tree blows the budget. path=subtree root (name, relative, or absolute /root/...); depth=levels (-1=all); max_nodes (default 250); max_children per node (default 50); collapse=true groups runs of identical leaf siblings (e.g. '8x CPUParticles2D'). Returns {tree, node_count, truncated?}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo
depthNo
collapseNo
max_nodesNo
max_childrenNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.12.1
  2. Removedv1.10.0
  3. First observedv1.0.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark the tool as read-only and non-destructive, and the description adds meaningful behavioral context: it returns a tree with node_count, may set a truncated flag, applies default limits, and can collapse identical leaf siblings. This goes well beyond the annotations and gives the agent a clear model of the tool's behavior.

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 dense but compact, front-loading the core purpose before diving into parameter details. Each sentence adds necessary information: purpose, token warning, parameter semantics, and return shape. There is no filler or repetition.

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

Completeness5/5

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

For a tool with no output schema and zero schema descriptions, the description is remarkably complete. It explains the live/runtime distinction, scoping constraints, all parameters with defaults, collapse behavior, and the return object shape. An agent has enough context to invoke this tool correctly and interpret results.

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?

Schema description coverage is 0%, so the description carries the full burden—and it delivers. Every parameter is explained: path, depth, max_nodes, max_children, and collapse, including defaults and example usage. This is exemplary parameter documentation for an otherwise bare schema.

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?

The description clearly states the tool dumps the live scene tree of the running game, with a specific verb and resource. It explicitly identifies itself as the runtime counterpart of get_scene_tree, which distinguishes it from a likely sibling. This is precise and easy for an agent to disambiguate.

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 clear practical guidance: scope the query to avoid token limits, and explains the control parameters. It references get_scene_tree as the counterpart, implying a usage distinction, but does not explicitly state when to prefer this tool over alternatives. The guidance is strong but not fully exhaustive.

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