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sweep_schedule_row

Takes a schedule row key, sweeps plan sheets for matching tag markers, counts verified matches, and optionally commits them as takeoff counts.

Instructions

Take off a schedule row's mark from the row itself — the estimator's own gesture: a transition type sometimes exists only as a schedule row plus tag markers scattered across the plan sheets, and this tool mints the condition FROM the row and finds every occurrence. Pass the row's key (e.g. 'T1') and the tool (1) reads the row from the set's schedule tables (the sheet_graph/find_schedule machinery — the row is the condition's cited source), (2) anchors a geometric fingerprint on the marker the tag is DRAWN as on a plan sheet (a deterministic pad ladder around the tag text; where the tag occurs more than once the fingerprint must recur at a second occurrence before it is trusted — anchor.corroborated), and (3) sweeps every PLAN-role sheet for it. The count is geometry AND text agreeing: drafting reuses one bubble shape across many marks, so a match counts ONLY when the row's own tag sits within the marker footprint (its bbox rides the match as tag_at evidence); a match labeled with a SIBLING row's tag is excluded and says whose it is, an unlabeled match is withheld as a question, and a tag drawn with no matching marker is disclosed as text_only. REFUSAL over guessing, with the reason and the fix: no such row; the same key in two tables (ambiguous); a tag drawn on no plan sheet; no repeatable marker linework around the tag — a fingerprint is never guessed from text alone (the fallback is always: marquee one instance with symbol_sweep). commit: true commits the counted matches as EA markers under the row's own key — one undo step for the whole set-wide sweep, every marker carrying origin.assignment {source: "schedule"} plus the anchor and row citation on origin.symbol.seed. The COUNT is scale-free (EA), but matching is not: where the anchor sheet and a target sheet both carry a scale, the marker is resized by their exact ratio before matching (scaled per sheet), and where one does not, the sweep runs at 1:1 and discloses it (scale_assumed) rather than reporting a confident zero. After committing, LOOK: view_sheet {overlay: true} over each swept sheet. Coordinates are image px at render scale 2.0: PDF pt × 2, origin top-left, y down (the browser canvas's native space). Sheet payloads carry dims in both px and pt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagYesThe schedule row's key exactly as drawn, e.g. 'T1', 'TR-2' — it becomes the condition tag on commit
commitNoCommit every counted match as one EA count marker (excluded/withheld/text_only never commit)
mirrorNoAlso match mirrored markers
rotationsNoAlso match 90/180/270-rotated markers
tolerance_pxNoEndpoint match tolerance in image px (default 2 — CAD jitter, not drift)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowYesThe schedule row the sweep was seeded from — the condition's source
tagYesThe row key as normalized (the tag as drawn)
noteNo
foundYesMatches carrying the row's own tag — the honest count, across every plan sheet
anchorYes
sheetsYesOne entry per swept PLAN-role sheet, load order
skippedYesSheets excluded from counting (schedule/detail/legend/unknown), each with its reason
warningNoPresent when the per-sheet work cap dropped candidates
completeYesTrue when every proposed placement was scored on every swept sheet — false means at least one sheet's count is a FLOOR, not a total (#261)
ea_totalNo
committedNocommit mode: count shapes committed — one per counted match, the whole sweep ONE undo step
conditionNocommit mode: the condition minted FROM the row — its key is the tag
shape_idsNo
Behavior5/5

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

With no annotations provided, the description carries the full transparency burden and surpasses expectations. It discloses commit semantics ('commits the counted matches as EA markers under the row's own key — one undo step'), refusal conditions, the `scale_assumed` disclosure instead of a confident zero, and coordinate-space details. The section on what counts vs. is excluded vs. withheld is particularly detailed and honest about the system's behavior.

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?

At ~400 words, this is a long description, but nearly every sentence earns its place — covering match-counting rules, scale behavior, refusal conditions, and coordinate systems without repetition. The density of unique information justifies the length, though a short example or heading structure could aid scannability.

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 tool with this level of algorithmic complexity — fingerprint anchoring, corroboration, scale handling — the description explains return evidence (`tag_at`), edge cases (`text_only`, `scale_assumed`), the fallback path, and even post-commit verification steps. The presence of an output schema offsets the need to document return values in detail, and the description fills in the behavioral gaps thoroughly.

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

Parameters4/5

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

Schema coverage is 100% across 5 parameters, satisfying the baseline-3 standard. The description enriches parameter understanding by explaining how `tag` becomes the condition tag, how `commit: true` interacts with match types (excluded/withheld/text_only never commit), and how `mirror`/`rotations`/scale affect matching. The parameter semantics benefit from the description, elevating it above the minimum baseline.

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 opens with 'Take off a schedule row's mark from the row itself' and details a specific verb+resource with an exact 3-step process (reads/binds the row, anchors a fingerprint, sweeps all PLAN-role sheets). It explicitly distinguishes itself from the sibling `symbol_sweep` by naming it as the fallback, which differentiates it from a tool in the same domain.

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 explicit context for when to use this tool — the estimator's gesture of a transition type living only as a schedule row plus tag markers — and lists concrete refusal/fix scenarios (no such row, ambiguous duplicate keys, no plan sheet, no repeatable linework). It also names the alternative `symbol_sweep` as the fallback path. It could go further in contrasting with additional siblings like `resolve_tag` or `find_schedule`, but the usage context is otherwise strong.

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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