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Galgame MCP

Galgame MCP

by YeXuanHs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

vnt_extract

Converts AI bundle directories into work/bundle.jsonl, organizing entries by ID for translation workflows. Ideal for AI-based Galgame localization projects.

Instructions

【AI bundle】目录→work/bundle.jsonl（按条目 ID 翻译）。不是「scn转json」。用户要经典每文件 .json 时用 vntextpatch_extractlocal。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "input": "<input>",
      "work": "<work>"
    },
    "name": "vnt_extract"
  }
}

Input Schema

NameRequiredDescriptionDefault
workYes
inputYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries full behavioral disclosure. It specifies the input (a directory) and output (work/bundle.jsonl), and indicates the processing logic ('按条目 ID 翻译' - translate by entry ID). It also explicitly disclaims a behavior it doesn't perform. However, it does not mention side effects like overwriting existing files, permissions, or detailed prerequisites, which prevents a perfect score.

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 description is extremely concise, consisting of two compact sentences. It front-loads the core purpose ('【AI bundle】目录→work/bundle.jsonl'), then adds a crucial negative clarification and an alternative. Every sentence serves a purpose with no wasted words.

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?

Given the tool's relative simplicity (2 params, no output schema, no annotations), the description covers the essential transformation, output path, and sibling differentiation. Some ambiguity remains around 'AI bundle' and the exact meaning of 'translate by entry ID', but overall it provides enough context for an agent to select and use the tool appropriately.

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 input schema has 0% description coverage, so the description must compensate. The phrase '目录→work/bundle.jsonl' implies that 'input' is the source directory and 'work' is the base directory for the output file, providing meaningful roles for both parameters. It does not explicitly define formats or types beyond this, but it adds substantial semantic value over the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: converting a directory to 'work/bundle.jsonl' with entry-ID-based translation. It also distinguishes itself from another sibling tool by explicitly saying it is not 'scn转json' and pointing to vntextpatch_extractlocal for per-file JSON output, demonstrating a specific verb+resource+scope and sibling differentiation.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: it states what the tool is not for ('不是「scn转json」') and gives a direct alternative ('用户要经典每文件 .json 时用 vntextpatch_extractlocal' - use vntextpatch_extractlocal when classic per-file .json is needed). This clearly communicates when to use this tool versus the identified sibling.

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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