InvoiceIn

Render invoice as HTML

invoice_to_html

Read-onlyIdempotent

Human-readable HTML rendering of the invoice (self-contained, printable), same layout for every syntax; labels in the requested language.

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	Default
`result`	Yes

Schema Changelog

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

First observedSep 7, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral detail beyond that: output is self-contained, printable, has the same layout for every syntax, and uses labels in the requested language. This enriches the agent's understanding of how the tool behaves.

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 with no filler. Every clause carries meaningful information: HTML rendering, human-readable, self-contained, printable, consistent layout, and language support.

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 simple read-only rendering tool with full schema coverage, annotations, and an output schema, the description covers the essential purpose and output traits. It could be slightly more complete by explicitly addressing when to choose this over sibling tools, but nothing critical is missing for correct invocation.

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 only a light connection between 'labels in the requested language' and the lang parameter, without adding semantics beyond what the schema already provides.

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 specific verb and resource: 'HTML rendering of the invoice'. It clearly distinguishes the tool from siblings by emphasizing HTML, self-containment, printability, consistent layout, and language labels — properties that set it apart from CSV, DATEV, reading, and validation tools.

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

Usage Guidelines3/5

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

The usage context is implied by 'Human-readable HTML rendering' and 'printable', which suggests when this tool is appropriate. However, the description does not explicitly state when to prefer it over invoice_to_csv, invoice_to_datev, read_invoice, or validate_invoice, nor does it name alternatives.

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

All InvoiceIn tools Inspect

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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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