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Galgame MCP

Galgame MCP

by YeXuanHs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

vnt_insert

Writes translated bundle.jsonl back into visual novel scripts, generating a separate output file for localized game data.

Instructions

【AI bundle】把 bundle.jsonl 译文写回脚本到独立 output。对应 vnt_extract；经典 JSON→scn 用 vntextpatch_insertlocal。

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>",
      "output": "<output>"
    },
    "name": "vnt_insert"
  }
}

Input Schema

NameRequiredDescriptionDefault
workYes
inputYes
outputYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
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 write-back mutation and the 'separate output' behavior, which signals non-destructive output targeting. However, it omits overwrite behavior, permissions, and failure modes, leaving gaps for a write operation.

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 two short sentences with no filler; the first sentence states the action and output, the second gives alternatives. It earns every word but uses shorthand [AI bundle] that may require context.

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

Completeness2/5

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

Given three required parameters with zero schema descriptions and no annotations/output schema, the description leaves important gaps. It provides workflow context and differentiation but doesn't explain what each parameter should contain or what the tool returns. This is insufficient for confident invocation without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and no parameter descriptions exist. The description mentions bundle.jsonl and script output but doesn't map these to the three parameters (input, work, output), so parameter usage remains ambiguous. It only hints that 'output' is the separate destination.

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 states a specific action: writing back bundle.jsonl translations into script files in a separate output. It explicitly differentiates itself from vntextpatch_insertlocal for classic JSON→scn and names its counterpart vnt_extract.

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?

It explicitly tells the agent when to use this tool versus alternatives: use it for 'AI bundle' workflow, and use vntextpatch_insertlocal instead for classic JSON→scn. The mention of corresponding vnt_extract clarifies the pairing.

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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