luhn_checkValidate any 12-19 digit number by the Luhn mod-10 algorithm (card-style checksums). Never submit real card numbers.
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | 12-19 digit number; spaces/dashes allowed. |
| Name | Required | Description | Default |
|---|---|---|---|
| tool | No | ||
| scope | No | ||
| valid | Yes | ||
| reason | Yes | ||
| country | No | ||
| normalized | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds the algorithm behavior (Luhn mod-10), the card-style domain, and a safety warning about real card numbers, going beyond what annotations provide.
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 short sentences with no filler. The core action and algorithm appear first, followed by a critical safety warning. 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 single-parameter, read-only, idempotent validator with an output schema, this description captures purpose, algorithm, acceptable input, and a critical real-world usage caution. No missing information would prevent correct invocation.
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?
Input schema covers 100% of the single parameter and already documents digit length and allowed spaces/dashes. The description reinforces the 12-19 digit scope and adds the algorithmic and privacy context, adding value beyond the schema.
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?
States a specific action ('Validate'), a precise input class ('any 12-19 digit number'), and the algorithm (Luhn mod-10). The 'card-style checksums' qualifier distinguishes it from sibling validation tools without needing to open schemas.
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?
Clearly implies the tool is for Luhn/card-style checksum validation and adds an explicit privacy guardrail ('Never submit real card numbers'). It does not explicitly name sibling alternatives or describe when not to use it, but the context is strong enough for correct selection.
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 distinct identifier type or specific function (e.g., IBAN vs. EIN vs. block info), with no overlap even among similar validation routines. The batch and rental tools combine distinct sub-checks without ambiguity, making misselection unlikely.
Tool names are uniformly snake_case and mostly follow a descriptive pattern, but the action verbs vary (e.g., 'check', 'format', 'info', 'guard', 'verdict') rather than a single verb_noun structure. While clearly readable, the pattern is not perfectly uniform.
At 28 tools, the server feels like a broad utility pack rather than a focused domain. The count exceeds the 'heavy' threshold and includes three distinct sub-domains (financial identifiers, rental fraud, on-chain queries), making the surface area hard to navigate coherently.
The identifier validation coverage is thorough (most common checksums), rental checks cover risk, deposit, and verdict, and on-chain tools handle balances, activity, and settlement history. Minor gaps exist (e.g., no token transfer history, no generic identifier detection) but agents can likely work around them.