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Unity MCP Server

Unity MCP Server

by rachitkumarrastogi
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TypeScript
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get_unity_ai_stack

Audit a Unity project's AI stack by checking Muse, Assistant, and Inference packages, AI-related ProjectSettings, and configuration hints without opening the Unity Editor.

Instructions

Unity AI platform audit: Muse/Assistant/Inference packages, AI-related ProjectSettings, hints.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_unity_ai_stack"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.2

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden, and it discloses almost nothing beyond a topic list. 'Audit' hints at a read-only scan, but the description never states whether it touches the filesystem, how deep the scan goes, or what 'hints' means in practice.

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?

One short, front-loaded line with zero filler, which is appropriately sized for a no-argument audit tool. It loses a point for being a telegraphic fragment rather than a readable sentence, which slightly increases the chance of misreading the scope.

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

Completeness2/5

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

With no annotations, no output schema, and no parameters, the description is the agent's only source of information, and it does not say what the audit returns or how it relates to the several near-duplicate AI siblings. That is a significant gap for a tool whose whole value is in its output shape.

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

Parameters4/5

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

The tool takes zero parameters, so the baseline of 4 applies; there is no parameter meaning for the description to add. The schema being empty also matches an audit that operates on the whole project.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The fragment names a recognizable scope (Muse/Assistant/Inference packages, AI-related ProjectSettings, hints) so an agent can guess it inventories the project's AI stack. But it is a noun phrase with no verb and no differentiation from siblings like get_ai_stack_summary or get_ai_ml_package_inventory, which sound like they cover overlapping ground.

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 statement of when to use this versus get_ai_stack_summary, get_ai_ml_package_inventory, or find_ai_related_scripts. The agent must infer selection from name overlap alone, and the description gives no exclusions or preconditions.

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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