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Unity MCP Server

Unity MCP Server

by rachitkumarrastogi
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TypeScript
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list_ml_agents_training_configs

Locate ML-Agents trainer YAML configuration files in a Unity project’s root and common folders to inspect available training setups.

Instructions

Find ML-Agents trainer YAML configs in project root and common folders.

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_agents_training_configs"
  }
}

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 behavioral burden. It discloses the search locations ('project root and common folders'), which is useful, but it does not clarify recursion depth, hidden-folder handling, return format, or whether it reads file contents versus only paths.

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?

The definition is a single front-loaded sentence with no filler or repetition. It states the action, target, and scope efficiently.

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 listing tool, the description is enough to know what is being searched, but it is thin for an agent interpreting results. It does not define 'common folders' or indicate whether the result is a list of paths, config names, or parsed YAML objects.

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 has zero parameters, so there is no parameter semantics to explain. The baseline for a parameterless tool applies, and the description does not need to add parameter meaning.

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?

The description states a specific verb ('Find') and resource ('ML-Agents trainer YAML configs') plus search scope ('project root and common folders'). It is clear what the tool does, but it does not distinguish this config search from sibling config-related tools such as list_ci_configs or list_ai_prompt_or_config_assets.

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 implied by the resource and location scope: use this when looking for ML-Agents trainer YAML configs. There is no explicit guidance on when to choose this over sibling listing tools or when not to use it.

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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