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netcafe-docs

by com.ainetcafe

A batch of invoices → one ledger-ready table (arithmetic-checked)

extract_invoices
Read-onlyIdempotent

Give it up to 20 invoice URLs (PDF or page images) and get back one table ready to post: number, date, seller, buyer, net / tax / gross, currency. Every row is checked in code — net + tax must equal gross — and the batch total is re-added independently, so a row the model misread is flagged with the exact difference instead of quietly landing in your books. Mixed currencies get no batch total on purpose: adding them together would be an accounting error. CSV is UTF-8 with BOM so Excel opens it right.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesInvoice URLs — comma-separated, or pass an array. Up to 20 per call.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses that every row is arithmetic-checked (net + tax = gross) and misreads are flagged with exact differences, which is not captured by the annotations. It also explains that mixed currencies produce no batch total to avoid accounting errors, and that CSV includes a UTF-8 BOM for Excel compatibility, providing valuable behavioral detail.

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 four sentences, front-loaded with the core action and followed by error-checking, mixed-currency, and formatting details. Every sentence adds unique information without redundancy, making it appropriately compact for the tool's complexity.

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 presence of an output schema and annotations, the description covers input constraints, processing behavior, and output encoding in detail. It mentions the ledger-ready fields, arithmetic validation, and Excel compatibility, ensuring a user understands what to expect from the tool.

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 describes the 'urls' parameter fully (comma-separated or array, up to 20), and the description adds that inputs can be PDFs or page images. Since schema coverage is 100%, the description adds only the file-type constraint, which is a modest enhancement over the 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 'Give it up to 20 invoice URLs (PDF or page images) and get back one table ready to post: number, date, seller, buyer, net / tax / gross, currency' clearly identifying the tool's function and output. It distinguishes from siblings like extract_tables by specifically targeting invoices with ledger-ready output and arithmetic checks.

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 clearly indicates the tool is for batch invoice extraction from URLs, implying usage for invoice processing. However, it doesn't explicitly mention when not to use it or suggest alternative tools like extract_tables for other documents, so it doesn't reach the highest bar for exclusionary guidance.

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

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