Hopi

Test card number generator

hopi_test_card_number_generator

Read-onlyIdempotent

Generate Luhn-valid TEST card numbers for QA and development, or validate a card number. Test numbers pass the Luhn checksum and match a real brand format but are NOT real accounts, are linked to no one, and cannot be charged. Generate mode returns numbers for a brand (Visa, Mastercard, American Express, Discover); validate mode returns whether a number passes Luhn and its detected brand. Runs on Hopi's server; nothing is stored and no bank is contacted. Source: https://hopi.co.uk/test-card-number-generator/

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	No	Generate test numbers, or validate a supplied number (default generate)	generate
`brand`	No	Card brand to generate (default visa)	visa
`count`	No	How many numbers to generate, 1 to 50 (default 10)
`number`	No	For validate mode: the card number to check (spaces and dashes are ignored)

Output Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes
`note`	Yes
`brand`	Yes
`count`	No
`numbers`	No
`summary`	Yes
`source_url`	Yes	The tool's page on hopi.co.uk (cite or open to check the figure)

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 20, 2026

addedOutput schema / properties / summary
```
Added value: +{
+  "type": "string"
+}
```

addedOutput schema / required

Added value: +[
+  "brand",
+  "mode",
+  "note",
+  "source_url",
+  "summary"
+]

First observedSep 20, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the readOnly and idempotent annotations, the description adds crucial behavioral detail: the numbers are not real accounts, cannot be charged, nothing is stored, and no bank is contacted. This fully discloses safety and external side effects.

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 front-loaded with the primary action, then safety facts, then mode behavior, with no filler. The source link is a minor addition that supports trust without bloating the text.

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 dual-mode tool with an output schema and rich annotations, the description covers purpose, modes, brands, safety constraints, server-side behavior, and data handling. Nothing essential for correct invocation is missing.

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 already documents mode, brand, count, and number. The description reiterates mode semantics and brand values but adds little parameter-level detail beyond what the schema provides, so baseline 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 names a specific verb and resource: generate Luhn-valid TEST card numbers for QA/development, or validate a card number. It clearly distinguishes the two modes and explicitly lists supported brands, so an agent can tell this from other Hopi generators/validators.

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?

It states the intended use case ('for QA and development') and explains when to use generate mode versus validate mode. It does not explicitly name alternative tools or exclusions, but the context is clear enough for a sibling-disambiguated selection.

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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