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sheet_graph

Indexes plan-set sheets to map rooms, schedules, details, and revisions, enabling agents to decide what to measure without manually enumerating rooms.

Instructions

The plan-set INDEX (#87): every sheet's role (plan / schedule / legend / …, with confidence and the title evidence), the schedule tables found (kind, row count, region — a schedule CONTINUED across sheets ("… SCHEDULE — CONT'D") reads as ONE table, the continuation fragment naming its base in "continues"; rotated column headers are read at their quarter-turn and flagged), every number CORROBORATED as a room (with the stacked room NAME when one exists, the room's BUILDING on multi-building sets, and "corroboration" saying why it counts as a room) plus "unmatched_tags" — the numbers that are NOT rooms (keynote hexagons, detail markers, dimension fragments, legend rows), each with a reason, listed and never dropped; READ those reasons, one of them may be a room the schedule left out, the detail callouts (3/A-601 → sheet edges), the set's building designators, every REVISION marker the set carries (text markers "Δ2"/"REV 2" AND drawn deltas — a bare digit inside a triangle of linework, proven from vector geometry and flagged drawn — in "revisions", and attached to the schedule row / room tag they sit on), and named indexing gaps in "notes". Built once per document from the text layer and cached. This is how an agent decides WHAT to measure without a human enumerating the rooms: list the rooms here, resolve each with resolve_tag, then measure with one_click/detect_rooms. A scanned set (no text layer) returns available: false — unavailable, never half-populated. 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

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNoNamed gaps found while indexing (e.g. a continuation whose rows could not be aligned) — the graph refuses silently dropping anything
roomsYesNumbers CORROBORATED as rooms — a room-finish row answers for them, or (where the set carries no room-finish schedule) a room name is drawn with them. Each says which in `corroboration`. Schedule sheets contribute rows, never phantom rooms
countsYes
sheetsYes
calloutsYesDetail callouts (3/A-601) — edges to their target sheets
availableYesfalse = the set has no text layer (a scan) — the graph degrades to unavailable, never half-populates
buildingsNoEvery building designator the set names (sorted) — present only on multi-building-aware sets. Room numbers reused across these need qualified tags ('A-134')
revisionsNoEvery delta-triangle / REV-tag marker the set carries — text markers ('Δ2', 'REV 2') and DRAWN deltas (a bare digit inside a triangle of linework, drawn: true) — where one sits, the ink changed under that revision. Markers on a schedule row or room bubble also attach there (and ride resolve_tag). A revision CLOUD is arc-chain linework these detectors do not read — absence here is not absence of revisions
unmatched_tagsNoNumbered tags on plan sheets that are NOT counted as rooms — keynote hexagons, detail markers, dimension fragments, legend rows. Listed with a reason, never dropped. A real finish plan is covered in 2–3 digit numbers that are not rooms; counting them as rooms makes every one come back "no schedule row", which reads exactly like the lost-bid case and buries it
Behavior5/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure — and it fully delivers. It reveals observable behavior well beyond the schema: caching and build-once semantics ('Built once per document from the text layer and cached'), atomic availability ('unavailable, never half-populated'), the exact pixel-vs-point coordinate system ('PDF pt × 2, origin top-left, y down'), and the way it treats table continuation and rotated headers. This is exactly the kind of context annotations would otherwise need to carry.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

This is a dense run-on wall of text — about 250 words with heavy parenthetical nesting, ALL-CAPS emphasis, and em-dash on em-dash — with no bullet points or line breaks. Every sentence does add unique information, but the format makes it difficult for an agent to scan or parse. This is the description's one real weakness: thorough to the point of being overwhelming with zero visual hierarchy.

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?

Given the high complexity of this tool (a full plan-set index with sheets, schedules, rooms, unmatched tags, detail callouts, revision markers, and building designators) plus an available output schema that it complements rather than duplicates, the description is remarkably complete. It covers content, failure modes, coordinate spaces, caching semantics, and pipeline position — leaving virtually no question about what the agent receives or when the tool is appropriate.

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?

With zero parameters, the baseline is 4 per calibration; the description goes well beyond the baseline by thoroughly documenting the rich return structure an agent must understand — including the shape of 'corroboration', the 'unmatched_tags' array with reasons, and the revision markers. Since there are no parameters to document, the tool's entire semantic weight is in its output, and the description maps that out completely.

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 is unambiguous about what this tool is: 'The plan-set INDEX (#87): every sheet's role (plan / schedule / legend...)' — a specific noun (index) with an enumerated scope (sheet roles, schedule tables, rooms, unmatched tags, revisions). It clearly distinguishes itself from siblings by positioning itself as the read/preparation step: 'This is how an agent decides WHAT to measure without a human enumerating the rooms.' The scope and resource are specific and concrete, not a restatement of the name.

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?

Explicit when-to-use and workflow guidance is provided: 'list the rooms here, resolve each with resolve_tag, then measure with one_click/detect_rooms.' The failure case is explicitly governed ('A scanned set (no text layer) returns available: false — unavailable, never half-populated'), and the warning to 'READ those reasons, one of them may be a room the schedule left out' gives the agent a concrete decision aid. Alternative sibling tools (resolve_tag, one_click/detect_rooms) are named directly.

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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