toolforte

VAT Calculator

calculate_vat

Read-onlyIdempotent

Add VAT to a net amount or extract VAT from a gross amount at any rate (e.g. 21 for Dutch BTW). Returns exact net, VAT, and gross amounts rounded to cents.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	add = amount is net, add VAT on top; extract = amount is gross, split out VAT
`rate`	Yes	VAT rate in percent
`amount`	Yes	The amount

Output Schema

TableJSON Schema

Name	Required	Description
`net`	No
`vat`	No
`rate`	No
`gross`	No
`result`	No	The result, when it is not an object

Schema Changelog

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

Changed1 schema field changedSep 5, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": {},
+  "properties": {
+    "gross": {
+      "type": "number"
+    },
+    "net": {
+      "type": "number"
+    },
+    "rate": {
+      "type": "number"
+    },
+    "result": {
+      "description": "The result, when it is not an object"
+    },
+    "vat": {
+      "type": "number"
+    }
+  },
+  "type": "object"
+}

First observedSep 1, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already mark the tool as read-only and non-destructive. The description adds meaningful behavioral detail by stating the exact outputs: 'net, VAT, and gross amounts rounded to cents.' This goes beyond the schema and gives the agent a clear expectation of the result.

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 exactly two sentences, with the core operation front-loaded and no filler. Every clause contributes value: the two modes, rate flexibility, a real-world example, and the return format.

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 simple, stateless calculation tool with full schema coverage, a read-only annotation, and an output schema, the description is complete. It states inputs, modes, rate range implication, and the rounded output values, leaving no significant gap for an agent to call it correctly.

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%, and the schema already fully documents amount, rate, and mode, including the meaning of the add/extract enum values. The description adds a small useful example ('21 for Dutch BTW') but does not meaningfully expand parameter understanding beyond the schema, so the baseline of 3 applies.

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: 'Add VAT to a net amount or extract VAT from a gross amount.' It also clarifies the rate flexibility with the example '21 for Dutch BTW', making the tool's purpose immediately obvious and distinct from the unrelated sibling tools.

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 gives clear context for when to use the tool by naming the two modes ('add' and 'extract') and the broad applicability ('at any rate'). It does not explicitly mention exclusions or compare to sibling tools, but no close alternative exists among the siblings, so the guidance is adequate.

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

A3.8/5.0

Disambiguation5/5

Every tool targets a distinct resource or action, and the detailed descriptions clearly separate near neighbors like generate_test_bsn versus generate_brp_test_data, read_page versus url_screenshot versus url_to_pdf, and image_compress/convert/resize. Even with 40 tools, there is no real boundary-blurring overlap.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes conventions: verb_noun (generate_*, validate_*), noun_verb (pdf_merge, image_resize), conversion-style names (csv_to_json, html_to_pdf), and bare nouns (base64, qr_code_png). The groups are recognizable, but there is no single predictable pattern.

Tool Count2/5

Forty tools is an oversized surface for an agent to consider on every call, well above the point where tool selection cost starts to hurt. The broad purpose explains the count, but many one-off utilities could be grouped or exposed selectively.

Completeness3/5

The server covers many domains—encoding, Dutch test data, image/PDF handling, memory, and workflows—but several categories are partial: there are no reverse conversions like json_to_csv or html_to_markdown, no PDF text extraction, and no workflow create/update/delete tools. Agents can work around some gaps, but notable operations are missing.

Resources

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