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Unity MCP Server

Unity MCP Server

by rachitkumarrastogi
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TypeScript
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get_ai_stack_summary

Summarizes a Unity project's AI stack—packages, model files, script matches, and ML-Agents configs—and can include prompt/RAG-like assets for a quick inventory.

Instructions

One-shot AI stack summary: packages, model files, script hits, ML-Agents configs. Set include_prompt_assets for prompt-like JSON/MD scan.

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_stack_summary"
  }
}

Input Schema

NameRequiredDescriptionDefault
include_prompt_assetsNoAlso scan for prompt/RAG-like assets

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.2

TDQS

B3.4/5.0
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 disclosure burden. It names the scan categories and the opt-in extra scan, but says nothing about cost/performance of a full-project scan, whether results are cached or truncated, or what the aggregate output looks like.

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?

Two short sentences, front-loaded with the summary scope and followed by the single option; nothing is padded. The telegraphic phrasing ("script hits") is slightly terse but still readable.

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

Completeness4/5

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

With no output schema and no annotations, the description does the important work of enumerating what the summary contains, which is the key thing an agent needs to decide to call it. It omits any indication of result size, ordering, or whether missing categories are reported as empty.

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?

Schema coverage is already 100%, so the baseline is 3, but the description adds real meaning by clarifying that the flag enables a prompt-like JSON/MD scan, specifying the asset file types the schema only calls "prompt/RAG-like".

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?

"One-shot AI stack summary" gives a specific verb+resource and the four enumerated categories (packages, model files, script hits, ML-Agents configs) tell the agent exactly what is aggregated, so it is distinguishable from narrow siblings like find_ai_related_scripts. It does not explicitly contrast with the similarly-named get_unity_ai_stack sibling, leaving some ambiguity between the two.

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?

"One-shot" implies this is the aggregate call to use when a broad picture is wanted, and the second sentence gives conditional guidance for the parameter. There is no explicit when-not guidance or named alternative for cases warranting a narrower scan.

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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