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Unity MCP Server

Unity MCP Server

by rachitkumarrastogi
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list_ai_skills

Lists bundled workflow skills organized by Unity domain such as scenes, prefabs, assets, project, code, AI-ML, and performance to help select the right skill for a task.

Instructions

List bundled workflow skills by Unity domain (scenes, prefabs, assets, project, code, ai-ml, performance, …).

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_ai_skills"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.2

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the burden. It does disclose that the skills are 'bundled' (i.e., come with the environment, nothing user-specific), which is useful context, but it says nothing about what the returned entries look like or whether their identifiers feed into read_ai_skill.

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; the domain list is the payload and arrives immediately. The trailing ellipsis signals non-exhaustiveness but is slightly vague about how many domains actually exist.

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 trivial zero-parameter listing tool with no output schema, the description covers purpose adequately. It is still incomplete on the one thing that matters here: the shape of the results (names, domains, ids) and whether those values are the inputs expected by read_ai_skill.

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 nothing for the description to disambiguate and the baseline of 4 applies. The parenthetical domain list usefully implies the categories the results are grouped under, which is more than the empty schema conveys.

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 ('List') and resource ('bundled workflow skills') and adds the organizing dimension ('by Unity domain'), with parenthetical examples that make the resource concrete. An agent can distinguish this from read_ai_skill, though the description does not explicitly contrast with siblings.

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: enumerating skills by domain suggests a discovery/browsing step, presumably before reading one. There is no explicit when-to-use, no exclusion, and no named alternative such as read_ai_skill, so an agent must infer the workflow position.

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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