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U-C4N
by U-C4N

Architecture: Schedule

arch_schedule

Creates door, window, or room schedule tables from drawing records, not caller input, with rows by tag and dimensions.

Instructions

Draw a door, window or room schedule as a real TABLE - a read of the drawing.

The rows come from the ACADMCP_ARCH records on the drawing (what arch_wall / arch_opening / arch_room write), never from the caller: doors by tag with width, height, swing, hand and wall; windows by tag with width, height, sill and wall; rooms by number with name and the measured area, plus a total. An opening drawn without a tag gets the next free D1 / W1 (K1 / P1 in Turkish) in wall order; an explicit tag is kept. A value the model does not carry is an empty cell, never a default. representation reports what was drawn: native (ACAD_TABLE, live) or composite (rules and MTEXT, headless).

Refused before anything is drawn: an unknown kind or lang (named with the list), nothing of that kind on the drawing, and more rows than one TABLE takes (198).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xYesWCS X of the table's top-left corner.
yYesWCS Y of the table's top-left corner.
kindYesdoors | windows | rooms
langNoHeaders and numbers: en | tr (decimal comma).en
scaleNoPlot-scale denominator: 50 is 1:50.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.6.0

TDQS

A4.3/5.0
Behavior5/5

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

With only destructiveHint=false in annotations, the description carries the load well: it discloses the destructive-safe read nature, the row source, untagged-opening auto-numbering, empty cells instead of defaults, the 198-row ceiling, and the native vs composite representation outcome.

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?

Front-loaded with the core action and the key constraint (records, not caller input) before enumeration of details. Dense but every clause carries information; slightly long, yet nothing is redundant filler.

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?

An output schema exists, so return values need not be described, and the definition still covers data source, refusal modes, and representation output. It stops short of covering edge behavior such as placement conflicts, but is otherwise complete for a 5-parameter, non-destructive generation tool.

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

Parameters3/5

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

Schema coverage is 100% and the schema already documents x, y, kind, lang, and scale. The description adds context about the K1/P1/D1/W1 tag convention and decimal-comma semantics for 'tr', but supplies no new syntax or format detail for the remaining parameters, so baseline 3 applies.

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?

States a specific verb+resource ('Draw a door, window or room schedule as a real TABLE') and clarifies it is a read of existing drawing records, distinguishing it from write tools like arch_wall/arch_opening/arch_room and from bom_table.

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

Usage Guidelines4/5

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

Establishes clear prerequisites and context: rows come only from existing ACADMCP_ARCH records, and it enumerates refusal conditions (unknown kind/lang, nothing of that kind on the drawing, >198 rows). It does not, however, explicitly compare itself to near alternatives such as bom_table or data_extract.

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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