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Drawing Converter (Metric ⇄ Imperial)

Extract a drawing PDF’s text layer

extract_drawing_text
Read-only

Read text tokens from a technical-drawing PDF page, providing each token's text, position, and a unit system guess to aid in classifying values for metric-imperial conversion.

Instructions

Read the printed text tokens of one page of a technical-drawing PDF: each token with its index, text and position (page points, top-left origin, rotation in degrees), plus page geometry and a heuristic guess of the drawing’s unit system. Use the token indices to build classifications for convert_drawing_pdf when you want to decide yourself which tokens are convertible values (e.g. mark a bare "3.2" next to a surface symbol as category=surface, or a title-block number as skip).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo1-based page number (default 1)
pathYespath to the drawing PDF
Behavior4/5

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds behavioral details: it returns token index, text, position, rotation, page geometry, and a heuristic unit system guess. It also notes it's per-page. No contradictions or hidden behaviors are present.

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 consists of two sentences: the first introduces the tool's output, and the second explains its purpose using token indices. Every sentence adds value with no padding. The structure is clear and front-loaded.

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

Completeness4/5

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

Given no output schema, the description explains the output in sufficient detail: token index, text, position, rotation, page geometry, and unit system guess. It also clarifies the relationship with convert_drawing_pdf. Missing error handling details, but for a read-only extraction tool, this is adequate.

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

Parameters3/5

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

The input schema covers 100% of parameters with descriptions for 'path' and 'page'. The description adds no new parameter-level details beyond stating that page is optional and 1-based (already in schema). Baseline 3 is appropriate as the schema already does the heavy lifting.

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 'Read the printed text tokens of one page of a technical-drawing PDF' specifies a clear verb ('read') and resource ('printing text tokens of a technical-drawing PDF'). It distinguishes itself from siblings like convert_drawing_pdf, which converts the whole PDF, and convert_value/convert_text, which handle individual values. The purpose is unique 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 explicitly states when to use this tool: 'Use the token indices to build classifications for convert_drawing_pdf when you want to decide yourself which tokens are convertible values'. It implies a specific workflow. However, it does not explicitly mention when not to use it or alternatives, though the sibling context makes differentiation clear.

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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