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netcafe-docs

by com.ainetcafe

PDF or scanned page → structured Markdown

pdf_to_markdown
Read-onlyIdempotent

Convert a PDF (or a scanned page image) into clean Markdown that keeps headings, lists and tables, and puts multi-column pages in the right reading order. Text-layer PDFs are read exactly and cost far less; images go through a vision model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic URL of the PDF, or of a page image (png/jpg) for scanned documents.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful behavior beyond annotations: exact text-layer extraction, vision model for images, cost differences, and reading order handling. It exceeds the baseline and does not contradict any annotation.

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?

Two tightly written sentences. The first sentence states the core action and scope, the second clarifies behavior and cost. No filler or repetition, and the most important information is 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 an output schema exists and annotations are present, the description covers input types, formatting preservation, multi-column handling, and cost/processing differences. It lacks explicit mention of file size limits or language support, but the provided information is sufficient for most use cases.

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 schema already defines the single 'url' parameter with 100% coverage and a clear description. The tool description does not add further parameter-specific detail beyond what the schema provides, so it meets the baseline but not more.

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 converts PDFs or scanned images into Markdown with formatting preservation and reading order. The verb 'convert' and specified output make the purpose unambiguous, and it distinguishes itself from sibling tools like extract_tables or translate_pdf.

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?

Provides clear context on when to use the tool (for text-layer PDFs vs scanned images) and notes cost differences, but does not explicitly name alternative sibling tools or exclusion criteria. This is more specific than generic guidance but stops short of explicit 'use this instead of X' instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All netcafe-docs toolsInspect
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TDQS

A3.7/5.0
Disambiguation5/5

Each tool targets a unique operation—conversions, extractions, translations, and utilities like resume checking or redaction—with no meaningful overlap. The few similar tools (e.g., convert_to_pdf vs. xlsx_to_pdf) are clearly distinguished by input type.

Naming Consistency3/5

Naming mixes conventions: verb_noun (extract_tables, redact_text), noun_to_noun (xlsx_to_pdf, pptx_to_pdf), and unusual forms like doc_translate_cn and what_can_you_do. While snake_case is consistent, the verb/noun pattern is not, making the set slightly less predictable.

Tool Count3/5

With 23 tools, the server sits at the heavy end of the acceptable range. Every tool has a distinct purpose, but the spread across PDF handling, research, audio, and accounting utilities feels more like a miscellaneous collection than a focused suite, which could overwhelm agents.

Completeness4/5

The server covers a broad spectrum of document-processing tasks—conversion, extraction, translation, redaction, and validation—with few dead ends. Minor gaps exist (e.g., no PDF merge/split, no OCR for all scanned PDFs, no explicit delete/update for resources), but core workflows are well supported.

Resources