USE THIS to check a payment card number's structure before using it — never assume a card number is valid or guess its brand. Verifies the Luhn checksum, detects the brand (Visa, Mastercard, Amex, Discover, Diners, JCB, UnionPay) from its BIN, and checks the length. Does NOT check whether the card is real, active or has funds.
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | The card number; spaces and dashes are ignored. |
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?
Discloses all relevant behaviors: Luhn checksum verification, brand detection from BIN, length check. States limitations upfront. Annotations confirm readOnlyHint=true, and description adds context without contradiction.
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?
Two sentences that are extremely efficient. Front-loaded with 'USE THIS' imperative, immediately stating purpose. No wasted words.
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?
Given the tool's simplicity (1 parameter, no output schema needed) and the comprehensive annotations, the description fully covers what an agent needs to use it correctly. No additional context is required.
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 already covers the single parameter with 100% coverage and description 'spaces and dashes are ignored'. Description adds semantic value by explaining what the validation involves (Luhn, brand, length), going beyond the schema's minimal type description.
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?
Description clearly states 'check a payment card number's structure' and specifies the resource (payment card number). It distinguishes from sibling validation tools by focusing on payment cards and their specific attributes (brand, Luhn, length).
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?
Explicitly says 'USE THIS to check a payment card number's structure before using it' and warns 'never assume a card number is valid or guess its brand'. Also clarifies what it does NOT check (real, active, funds), guiding when not to rely on it.
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.
Each tool targets a specific identifier or operation (e.g., validate_iban, parse_date, is_holiday). Even similar tools like validate_isbn and validate_isbn10 are distinct by version. There is no overlap or ambiguity.
Most tools follow a verb_noun pattern (validate_xxx, parse_xxx, format_currency). The exception is 'next_holiday', which uses an adjective instead of a verb. Otherwise consistent.
With 47 tools, the set is very large. While each tool is distinct, the count exceeds the recommended range (25+ is considered too many) and may overwhelm users or agents.
Covers a wide array of international identifiers and utilities, but notable gaps exist (e.g., no Canada SIN, India PAN, Mexico CURP). The set is broad but not exhaustive for the domain of data validation.