vat_calculatorAdd VAT to a net amount or remove it from a gross amount. Runs on smart-tools.xyz.
| Name | Required | Description | Default |
|---|---|---|---|
| rate | Yes | VAT rate, percent | |
| amount | Yes | The amount | |
| locale | No | Language for the source_url link (default en) | |
| direction | No | add: amount is net → gross; remove: amount is gross → net (default add) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the burden. It discloses the two core operations (add/remove) but does not mention output format, rounding behavior, or any other behavioral traits. This is acceptable for a basic calculator but still leaves ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The first sentence is concise and front-loaded with the tool's purpose. However, the second sentence 'Runs on smart-tools.xyz' adds no selection or invocation value, so not every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple calculator with full schema coverage and no output schema, the description is minimally adequate. It omits details about return values, rounding, or precision, which could matter for VAT calculations, but the core purpose is clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the schema already describes each parameter (amount, rate, locale, direction) with enums. The description adds little beyond restating the add/remove semantics found in the 'direction' parameter description, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with a specific verb+resource: 'Add VAT to a net amount or remove it from a gross amount.' This clearly distinguishes the tool from sibling calculators (e.g., percentage_calculator, discount_calculator) by naming VAT as the resource and both supported directions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: when you need to add VAT to a net amount or remove VAT from a gross amount. It does not explicitly name alternatives or when-not conditions, but the two modes are sufficient guidance for a simple calculator.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct, but there are notable overlaps. 'hash_generator' and 'file_hash' effectively do the same thing (SHA hashing of text), and several CSV/TSV converters (csv_to_json, csv_to_sql, table_to_csv, markdown_table) have similar input handling, though outputs differ. The sheer number of converters is clear, but these near-duplicates create some ambiguity.
Names follow a generally consistent pattern: lowercase with underscores, often ending in 'converter', 'calculator', or 'generator'. A few names like 'base64', 'lorem_ipsum', and 'transliteration' deviate from the suffix pattern, but the naming style is uniform and predictable overall.
With 73 tools, this server is far over the typical well-scoped range. While the broad utility-toolkit purpose might justify a larger set, 73 is unwieldy and pushes well into 'too many' territory, making it harder for an agent to select the right tool efficiently.
The server covers a remarkably wide range of common utilities: unit conversions, calculators, text transformations, development helpers (JSON, regex, JWT, subnet), and generators. Minor gaps exist (e.g., currency converter, time zone converter), but for a general-purpose toolkit, it is largely comprehensive.