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Unity MCP Server

Unity MCP Server

by rachitkumarrastogi
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TypeScript
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get_ai_ml_package_inventory

Lists Unity AI/ML packages in the project manifest, including ML-Agents, Sentis, Barracuda, AI Navigation, Muse, and Unity AI, so you can audit installed AI capabilities without opening the Editor.

Instructions

List Unity AI/ML packages in manifest (ML-Agents, Sentis, Barracuda, AI Navigation, Muse, Unity AI).

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_ai_ml_package_inventory"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.2

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full disclosure burden. "In manifest" hints that results come from the manifest file rather than installed packages, which is useful, but it never says the tool is read-only, whether packages must be installed to appear, or what the response contains.

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?

A single front-loaded sentence with no filler, and the parenthetical list is efficient. It is arguably slightly too terse for an agent deciding between overlapping AI-stack tools, but nothing is wasted.

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?

For a zero-parameter inventory tool with no output schema, the description covers the core purpose but leaves the return shape (counts, versions, enabled flags) and the manifest-vs-installed distinction unstated. Adequate, not complete.

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 there is no parameter meaning to add and the baseline of 4 applies. The description's brief mention of what qualifies as an AI/ML package is a minor bonus.

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?

Names a specific verb ("List") and a specific resource ("Unity AI/ML packages in manifest"), and enumerates the covered packages (ML-Agents, Sentis, Barracuda, AI Navigation, Muse, Unity AI). This is enough to separate it from sibling inventories like get_optimization_package_inventory or get_llm_runtime_inventory, though it never explicitly states the contrast.

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 or when-not-to-use guidance, and no mention of nearby siblings such as get_unity_ai_stack, get_ai_stack_summary, or get_package_dependency_graph that an agent could confuse this with. Usage is only implied by the word "List".

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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