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o3de-mcp

o3de-mcp

by nickschuetz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_transform

Fetch an entity's world transform, including position, rotation quaternion, effective scale, and uniform/non-uniform scale components for O3DE scene inspection.

Instructions

Get the world transform of an entity.

Returns position, rotation (an [x, y, z, w] quaternion) and scale, the effective [x, y, z] scale: the Transform's uniform scale times the entity's Non-uniform Scale component, if it has one. uniform_scale and non_uniform_scale (null without the component) give the two parts.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "entity_id": "<entity_id>"
    },
    "name": "get_transform"
  }
}

Input Schema

NameRequiredDescriptionDefault
entity_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

B3.3/5.0
Behavior4/5

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

With no annotations to lean on, the description still discloses meaningful semantics beyond structure: rotation is returned as an [x, y, z, w] quaternion, scale is the effective scale computed as uniform times the Non-uniform Scale component, and uniform_scale/non_uniform_scale are null when that component is absent. It does not cover error behavior for an invalid entity_id or permission requirements, which keeps it out of 5 territory.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose sentence is front-loaded and the return-value detail follows in a compact block. Since an output schema exists, some of the return description is redundant, but it is short enough not to be wasteful.

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?

For a single-parameter read getter with an output schema and no annotations, the description supplies the semantics the schema cannot (coordinate frame, quaternion ordering, effective-scale derivation). The only real omission is any hint about entity_id's format or what happens when the entity does not exist.

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

Parameters2/5

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

Schema description coverage is 0% for the single required parameter entity_id, so the description must carry the burden and does not. It never explains what an entity_id is, what format it takes, or how to obtain one (e.g., via list_entities or get_entity), leaving the only input undocumented.

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 verb and resource ('Get the world transform of an entity') and clarifies scope with 'world' transform, which implicitly separates it from the local-space behavior an agent might assume. It does not explicitly name set_transform as the write counterpart, but the read/write split is unambiguous from the verb.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no prerequisites, and no reference to alternatives despite set_transform being the obvious inverse operation in the sibling list. The agent must infer the context of use entirely from the tool name.

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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