InvoiceIn

Read invoice

read_invoice

Read-onlyIdempotent

Parse any European e-invoice into canonical EN 16931 JSON and validate it against the official rule sets. Returns the detected format, the invoice as one JSON shape regardless of syntax, and a validation report whose failed rules carry plain-language fix hints.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lang`	No	Language of the fix hints and rendering labels: en, de, pl, it or fr.	en
`path`	No	Local file path instead of file_base64; only honoured when the server runs over stdio on the same machine.
`file_base64`	No	The invoice file, base64-encoded: XML (UBL, CII, XRechnung, Peppol, FatturaPA, KSeF FA(3)) or a ZUGFeRD/Factur-X hybrid PDF. Up to 25 MB decoded.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes
`error`	No	Present when ok is false: code, message, hint
`source`	No
`invoice`	No	Canonical EN 16931 JSON: document, seller, buyer, lines, tax_breakdown, totals, payment, attachments, extensions
`timings_ms`	No
`validation`	No

Schema Changelog

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

First observedSep 7, 2026

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds useful behavioral detail: it returns a detected format, a canonical unified JSON shape, and a validation report with plain-language fix hints. This goes beyond the annotations without contradicting them.

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 two sentences, front-loaded with the core purpose, and each sentence carries meaningful information: what it parses, what it validates, and what it returns. There is no filler or redundant restating of the tool name.

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?

For a read-only parsing tool with full schema coverage and an output schema, the description is largely complete. It covers purpose, output shape, and validation behavior; the main shortfall is not clarifying how this relates to the validate_invoice sibling or when the conversion siblings are the better choice.

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?

Schema description coverage is 100%, so the schema fully documents lang, path, and file_base64. The description adds high-level context about European e-invoice parsing but does not add parameter-level semantics beyond what the schema already provides, so the baseline 3 applies.

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 verb ('Parse any European e-invoice into canonical EN 16931 JSON') and clearly identifies the resource and output. It also mentions validation, but it does not explicitly distinguish itself from the sibling validate_invoice, which could also validate invoices.

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?

The description gives no guidance on when to choose this tool over alternatives like validate_invoice or the invoice_to_* converters. The mention of returning JSON implicitly suggests different output formats are handled elsewhere, but there is no explicit when/when-not 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

A3.9/5.0

Disambiguation4/5

The three conversion tools are clearly separated by output format, and the parse versus validation distinction is mostly clear. The main overlap is between read_invoice and validate_invoice since both produce validation reports, though the descriptions clarify that read_invoice returns the full canonical invoice while validate_invoice is a cheaper validation-only operation.

Naming Consistency4/5

The conversion tools follow a consistent invoice_to_<format> pattern, while read_invoice and validate_invoice use a verb_invoice pattern. This is readable and predictable, but slightly inconsistent because the conversions do not follow the same verb-first style.

Tool Count5/5

Five tools is well-scoped for an invoice processing server: parsing, validation, and three common output conversions. Each tool has a distinct role, and the count does not feel padded or thin.

Completeness4/5

The server covers the core invoice workflow: read, validate, and convert to useful outputs. Minor gaps exist, such as no explicit listing of supported source formats or batch/multiple invoice handling, but these are not critical for the apparent purpose.

Resources

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