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Unity MCP Server

Unity MCP Server

by rachitkumarrastogi
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TypeScript
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list_ml_model_assets

Lists ML model files stored under Unity Assets (.onnx, .nn, .pt, .tflite, .pb), helping you locate model assets without opening the Unity Editor.

Instructions

List ML model files under Assets (.onnx, .nn, .pt, .tflite, .pb).

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": "list_ml_model_assets"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.2

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; it does disclose the search scope (Assets) and the extension whitelist, which is real behavioral context. It omits whether the scan is recursive, whether results include paths/GUIDs, and any cost or size limits for a whole-project scan.

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?

A single short sentence, front-loaded with the verb and resource, and the extension list is compact. Nothing redundant or padded.

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 simple zero-parameter listing tool this covers the essential scope, but with no output schema and no annotations the description should say something about the shape of the result (paths vs. asset names) and whether the search is recursive. Those gaps are small but real for an agent that must decide whether to call it.

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 semantics to explain and the baseline is 4. The enumerated extensions in the description describe the implicit filter rather than any input the agent controls.

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 ("List ML model files") plus the scope ("under Assets") and the exact extensions matched, so an agent knows precisely what set of files comes back. It does not differentiate itself from the closely related sibling list_assets_by_extension, which appears to do a similar filtered listing.

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

Usage Guidelines3/5

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

Usage is only implied: an agent can infer this is the tool for finding ML model files, but there is no statement of when to prefer it over generic asset listing or extension-based listing tools. No exclusions or preconditions are given.

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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