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pdf_to_text

PDF to Text — COPY THE WORDS OUT of a PDF: get the wording, sentences and paragraphs as plain text you can paste into an email, a document or a spreadsheet. Extract the text that is already inside a PDF and return it as a plain .txt file. Reads the PDF's existing text layer using pdftotext with a Ghostscript txtwrite fallback — it does NOT run OCR. A scanned or photographed document has no text layer, so this tool refuses it with a 422 naming pdf_ocr rather than returning an empty file; run pdf_ocr first to add a searchable text layer, then extract. Mixed documents still succeed: pages that yielded no text are reported in the X-Conversion-Notes response header instead of being dropped silently. [category: pdf]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileYesInput file (PDF)

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

The description discloses important behavioral details beyond the annotations: it uses pdftotext with a Ghostscript txtwrite fallback, refuses scanned documents instead of returning an empty file, and reports empty pages via the X-Conversion-Notes response header. This gives the agent a clear mental model of success and failure modes.

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 front-loaded with purpose and then provides implementation and error-handling details. There is minor redundancy between the first and second sentences, but each additional sentence contributes useful behavioral context, so it remains appropriately sized.

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?

For a one-parameter conversion tool with no output schema, the description is complete: it explains what the tool returns, how it works, when it will fail, what error to expect, and what the caller should do in that failure case. Nothing needed for correct selection or invocation is missing.

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?

The schema already documents the single 'file' parameter as an input PDF with 100% coverage. The description adds meaningful context by explaining that the PDF must contain an existing text layer and that scanned files will be rejected, which is valuable semantic information beyond the raw schema.

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 states a clear verb-resource pair: extract the text already present in a PDF and return it as a plain .txt file. It explicitly says it uses the existing text layer and does NOT run OCR, which distinguishes it from OCR-based tools like pdf_ocr and photo_to_text.

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 gives explicit when-to-use guidance and names an alternative: if the PDF is scanned or photographed and has no text layer, the tool returns a 422 with a reference to pdf_ocr, and the agent is told to run pdf_ocr first. It also covers mixed documents, saying pages with no text are reported rather than silently dropped.

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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TDQS

B3.2/5.0
Disambiguation2/5

Multiple tool pairs are near-identical: octopus_mkdir/octopus_make_folder and octopus_move/octopus_move_file are literal duplicates, analyze_hash/generate_hash both compute hashes, convert_word_to_pdf overlaps convert_document, and photo_compress/photo_compress_to_size plus pdf_thumbnails/pdf_to_images have fuzzy boundaries. The descriptions are detailed and cross-reference each other helpfully, but at 144 tools an agent will regularly misselect.

Naming Consistency3/5

The dominant {category}_{verb}_{object} snake_case pattern (pdf_*, photo_*, convert_*, analyze_*, media_*) is largely consistent and predictable. However, outliers like chatwithyourpdf and describe_image break the category-prefix convention, and the octopus namespace mixes bare verbs (read, write, mkdir) with verb_noun forms (make_folder, move_file, search_meta) inconsistently.

Tool Count2/5

144 tools is an extreme count for any MCP server. The broad scope (PDF, photo, video, audio, conversion, analysis, generation, file storage, web, e-sign) justifies some volume, but the count is inflated by batch and inspect variants (pdf_to_excel + batch + inspect), duplicate tools, and overlapping converters. An agent faces an overwhelming selection surface.

Completeness4/5

Per-domain coverage is remarkably deep: PDF spans merge/split/compress/protect/unlock/metadata/OCR/watermark and bidirectional conversion; photo covers editing, format conversion, face handling, OCR, and collage; file storage has full CRUD plus search. Minor gaps exist (no audio transcription, no video metadata editing, no deletion of PDF pages is actually covered via pdf_delete_pages) but the surface has no dead ends for its declared domains.

Resources