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derive_base

Turn room perimeters into wall base takeoff quantities by deducting stated door openings, committing net linear footage per room.

Instructions

Mint the wall base from committed rooms (#148) — the estimator's most mechanical derivation: base LF = room perimeter − stated door openings. For every floor_area shape of source_condition, commits ONE linear shape under condition (e.g. 'RB-1') tracing that room's boundary, quantified NET of the openings you state per room. The openings are YOUR claim to make — look at the doors with view_sheet, state {shape_id, lf} per room (repeat a shape_id to stack openings); the tool never guesses, and your claim is recorded on origin.derived (from_shape_id, gross_lf, openings_lf). All-or-nothing: an unknown shape_id, a negative lf, or openings meeting a room's whole perimeter refuses the call before anything commits. The whole derivation is ONE undo step. Deriving onto the source condition is refused — base lands on its own tag.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
openingsNoStated openings per room — omit for gross perimeters
conditionYesFinish tag the base commits under (minted on first use), e.g. 'RB-1'
source_conditionYesFinish tag whose floor_area rooms the base derives from, e.g. 'CPT-1'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
roomsYes
total_lfYesSum of net_lf across rooms
committedYes
conditionYesThe tag the base committed under
source_conditionYes
Behavior5/5

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

No annotations are provided, so the description must fully disclose behavior. It does: commits one shape per room, records the claim on origin.derived (from_shape_id, gross_lf, openings_lf), never guesses, all-or-nothing atomicity, one undo step, and refusal to derive onto source condition. This is exceptionally transparent and leaves no ambiguity about side effects.

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 dense but every sentence earns its place. It front-loads the core purpose and formula, then methodically covers workflow, data recording, validation, undo behavior, and constraints. There is no fluff or repetition; the structure mirrors the natural sequence of using the tool.

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

Completeness5/5

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

For a complex tool with 3 parameters, no annotations, and an output schema (not shown), the description is highly complete. It covers the full lifecycle: prerequisites (committed rooms), input specification (openings per room), side effects (commit, recording on origin.derived), failure modes (all-or-nothing refusal), and constraints (own tag). Nothing necessary is missing.

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

Parameters5/5

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

Despite 100% schema coverage, the description adds significant meaning beyond the schema: it explains how to stack openings by repeating shape_id, states that openings meeting a room's whole perimeter are refused, and clarifies the 'claim' semantics (your claim to make, recorded on origin.derived). This enriches the raw parameter definitions substantially.

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 precisely states the tool's function: 'Mint the wall base from committed rooms (#148)' with a clear formula (base LF = room perimeter − stated door openings) and details that it commits one linear shape per room. This distinguishes it from sibling tools like measure_polygon or list_shapes by emphasizing the derivation/commit workflow and the 'base lands on its own tag' behavior.

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?

The description provides clear workflow context: use view_sheet to inspect doors, then state openings per room. It also states when not to use it (deriving onto the source condition is refused) and describes all-or-nothing validation. However, it doesn't explicitly name alternative tools for manual measurement or other derivation types, so it stops short of full when/when-not guidance.

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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