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find_text

Locate a known text string on a plan sheet and get its center coordinates to seed one-click area tracing. Supports case-insensitive substring matching and optional region restriction.

Instructions

LOCATE a known string on a sheet — the complement to read_sheet_text (which returns what a region SAYS; this finds WHERE a string you already know sits). Case-insensitive substring match against each pdf.js text run, so a room label split across runs ("OFFICE" then "134" as separate items) needs a find_text call per fragment, or read_sheet_text over a region to see the whole thing joined. Every hit's center feeds straight into one_click as the seed — the locate-then-trace workflow: find_text the room number, one_click at (or just past) its center. Optionally restrict to a region {x0, y0, x1, y1}; results cap at limit (default 200), with count/truncated telling you exactly how much a tighter region or higher limit would recover. 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
qYesText to find — a room number ('134'), a label fragment ('RECEPTION'), a schedule tag ('CPT-1')
limitNoMax hits returned
sheetYes
regionNoRect in image px (origin top-left, y down); omit for the full sheet

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYes
hitsYes
countYesTotal matches before the limit cap
sheetYes
truncatedYestrue = count exceeds hits.length; narrow the region or raise limit
Behavior5/5

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

With no annotations provided, the description fully carries the behavioral disclosure burden. It details case-insensitive substring matching, pdf.js text run splitting, coordinate system (image px at render scale 2.0, origin top-left, y down), result capping, and count/truncated fields. This is comprehensive.

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 a single, information-dense paragraph. It front-loads the purpose and workflow, but could be more structured with bullet points or sections. However, every sentence adds value, and there is no redundancy.

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 existence of an output schema (not shown), the description does not need to detail return values. It covers coordinate system, region restriction, limit behavior, and workflow integration. For a find_text tool, this is complete and actionable.

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 75% (3 of 4 parameters described). The description adds value beyond the schema by explaining coordinate semantics for the region parameter, case-insensitive behavior for q, and default/max behavior for limit. The undocumented sheet parameter is minor.

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 clearly states the tool's function: 'LOCATE a known string on a sheet' and explicitly distinguishes it from the sibling tool read_sheet_text, which returns what a region says. The verb 'locate' and resource 'string on a sheet' are specific and unambiguous.

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 usage context, including the locate-then-trace workflow with one_click, and explains the behavior for fragmented text runs. It does not explicitly state when not to use it, but the guidance is strong and the scenario is concrete.

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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