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v8unpack-mcp

v8unpack-mcp

by sergeyfedyakov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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read_dcs

Read 1C data composition schema layouts: get an overview of data sets, parameters, and variants, or retrieve a specific data set's query and fields, or a variant's raw XML settings.

Instructions

Прочитать схему компоновки данных (СКД) макета. Уровень 1 (без data_set/variant) — обзор: data_sets (имя/тип), parameters, variants (имя/представление). data_set='Имя' — текст запроса и список полей набора. variant='Имя' — сырой XML настроек варианта (как хранится в .bin). Только чтение

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": {
      "dir_path": "<dir_path>"
    },
    "name": "read_dcs"
  }
}

Input Schema

NameRequiredDescriptionDefault
variantNo
data_setNo
dir_pathYes
object_pathNo
template_nameNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It explicitly states the operation is read-only and describes exactly what each mode returns: overview, query with fields, or raw variant XML as stored in .bin. This is strong transparency, though it does not address error behavior or prerequisites.

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 compact and front-loaded: purpose first, then mode-specific behavior. Every sentence adds useful information, and the use of semicolons keeps the three modes scannable without unnecessary prose.

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?

The description provides good operational detail for the main read modes and an output schema exists to cover return shapes. However, the required parameter dir_path and two additional schema properties are left unexplained, and there is no guidance for choosing this tool among siblings, creating noticeable gaps.

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 description coverage is 0%, so the description must explain all parameters. It covers data_set and variant well, but does not explain the required dir_path parameter nor the additional object_path/template_name parameters that appear in the schema. This leaves significant parameter semantics undocumented.

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 clearly states the tool reads a data composition scheme (DCS) of a layout, and enumerates the three modes of operation. It does not explicitly distinguish itself from sibling tools such as read_template or export_dcs, so it misses the sibling-differentiation criterion for a 5.

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 description implies the tool is for read-only inspection and explains how data_set and variant influence the result. However, it does not say when to choose this tool over alternatives or mention any exclusions, so the guidance is implied rather than explicit.

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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