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Galgame MCP

Galgame MCP

by YeXuanHs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

image_probe

Detect native image formats (PNG, TLG, etc.) in visual novel files without vision/AI, enabling precise format identification for localization tasks.

Instructions

Detect PNG/TLG and other native image formats without vision/AI.

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": {
      "path": "<path>"
    },
    "name": "image_probe"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior2/5

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

There are no annotations, so the description must disclose behavior. It only states what the tool detects, not whether it is read-only, what it returns, or any limitations. No details on side effects, errors, or performance characteristics are provided, leaving the agent underinformed.

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 a single, concise sentence that front-loads the core function. Every word is purposeful, with no filler or redundancy.

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 simple one-parameter probe tool with no output schema, the description gives the essential purpose but omits any information about return values, invocation context, or relationship to sibling probes (e.g., audio_probe, archive_probe). It is minimally complete but lacks the context an agent needs to confidently invoke it.

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 the description does not mention the 'path' parameter at all. While 'path' is superficially self-explanatory, the tool description provides no guidance on expected input (e.g., file vs. directory, supported extensions, handling of non-existent paths). The description fails to compensate for the missing schema documentation.

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: 'Detect PNG/TLG and other native image formats' with a specific verb ('Detect') and resource ('image formats'). It also distinguishes from siblings like image_convert and image_pack_xp3 by explicitly noting 'without vision/AI', making the tool's niche clear.

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?

The phrase 'without vision/AI' implies a lightweight, pure-signature-based detection use case, but no explicit alternatives or when-not-to-use scenarios are mentioned. Usage is implied rather than stated, which is a clear gap compared to tools that name alternative tools.

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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