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twoer

pdf-toolbox-mcp

by twoer

Tool Extract Text

tool_extract_text

Extract text from PDF files by specifying page ranges like '1-3,5', preserving layout, or returning each page separately. Get readable text content from PDFs for further processing.

Instructions

提取 PDF 文本(pdftotext)。pages 精确页范围如 '1-3,5';layout 保持版面;per_page 按页返回。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
pagesNo
layoutNo
per_pageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.1

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description bears the behavioral disclosure burden. It adds useful parameter behavior — exact page-range syntax, layout preservation, and per-page return mode — but it omits broader traits such as that pdftotext only reads the embedded text layer, that scanned PDFs may return empty text, and that the file is not modified.

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 is a single front-loaded sentence that names the operation first and then gives a compact semicolon-separated parameter summary. Every phrase adds relevant information without redundancy.

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

Completeness3/5

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

For a four-parameter tool with no annotations, the description covers the main extraction options and an output schema exists for return values. However, it does not cover the critical decision boundary between this tool and OCR-based text extraction, nor what happens when pages is null, leaving the agent with partial context for correct invocation.

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 description coverage is 0%, so the parameter-level details in the description are essential. It explains pages with an example, layout as preserving layout, and per_page as returning by page; only path is left implicit, but its meaning is clear from the tool context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific operation: extract PDF text via pdftotext, with a clear verb and resource. It does not explicitly differentiate itself from sibling OCR tools like tool_ocr_pdf that can also produce text from PDFs, relying on the pdftotext reference to imply embedded-text extraction.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives such as tool_ocr_pdf for scanned PDFs or tool_locate_text for finding text positions. The description only explains parameter behaviors, not the use-case conditions that should select this tool.

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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