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sweep_schedule_row

Finds every instance of a schedule row's tag across plan sheets, confirming each match by both marker geometry and label text, then commits counted matches as EA markers.

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
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, the description carries full responsibility and excels: it discloses the multi-step algorithm, refusal conditions, commit behavior with undo semantics, scale handling (scaled/scale_assumed), coordinate system, and output evidence fields. There is no contradiction with annotations (none provided).

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?

The description is long but front-loaded with purpose and structured into clear steps. Every sentence delivers technical information and earns its place, though the density is heavy. It is appropriately verbose for the tool's complexity, but not as concise as ideal.

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?

The description covers input, process, refusal, commit, scale assumptions, coordinate spaces, and return fields (anchor.corroborated, tag_at, scaled, scale_assumed, text_only). Given the tool's complexity and lack of annotations, this is fully complete; the output schema exists and the description complements it.

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%, so the baseline is 3. The description adds meaningful context for 'tag' (the row's key becomes the condition tag) and 'commit' (commits counted matches as EA markers with origin metadata). It does not elaborate on mirror, rotations, or tolerance_px beyond their schema descriptions, so not a 5.

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 a specific verb and resource ('Take off a schedule row's mark from the row itself') and clearly explains the tool sweeps plan sheets for occurrences of the row's tag. It distinguishes itself from siblings by naming alternative fallback (symbol_sweep) and contrasting with find_schedule/sheet_graph machinery.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool ('a transition type sometimes exists only as a schedule row plus tag markers scattered across the plan sheets') and gives an alternative: 'the fallback is always: marquee one instance with symbol_sweep'. It also details refusal cases and fixes, providing clear decision 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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