USE THIS to verify a South African ID number before relying on it. Checks the Luhn check digit and date-of-birth validity, and returns the date of birth, gender and citizenship status encoded in the number.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The 13-digit South African ID number. |
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?
Annotations indicate this is a read-only operation, and the description adds details on the specific checks (Luhn, DOB validity) and outputs (DOB, gender, citizenship). No contradictions with annotations.
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, front-loaded with the imperative usage instruction, followed by concise technical details. No extraneous content.
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 validation tool with one parameter and no output schema, the description fully covers the tool's behavior, inputs, and outputs. The sibling validator list provides sufficient context.
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 coverage is 100% with a clear parameter description. The description adds minor extra context (stating '13-digit') but does not significantly enhance understanding 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?
The description explicitly states the tool verifies a South African ID number, checking Luhn check digit and date-of-birth validity, and returning encoded data. This clearly defines the specific resource and action, distinguishing it from sibling validators for other ID types.
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 tells the agent to use this before relying on a South African ID number. While it does not explicitly list when not to use it, the sibling tools context and tool name make it clear this is specific to SA IDs.
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.