USE THIS to verify a Belgian National Register Number (Rijksregisternummer / Numéro de Registre National) before relying on it. Checks the 11-digit form and the mod-97 check (handling the born-from-2000 rule). Validates structure only.
| Name | Required | Description | Default |
|---|---|---|---|
| nrn | Yes | The 11-digit Belgian National Register Number (punctuation 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?
Annotations show readOnlyHint=true, and the description confirms no side effects (validates structure only). It adds specific behavioral details like handling the born-from-2000 rule in mod-97 checks, which goes beyond the annotation while being consistent.
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 description is three sentences, front-loaded with the imperative 'USE THIS' and no unnecessary words. Every sentence provides distinct information: what to do, what it checks, and what it validates.
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 (one parameter, read-only, no output schema), the description is fully complete. It covers the purpose, checks performed, and limitation (structure only). No gaps remain.
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 description for the single parameter. The tool description mentions punctuation is ignored, which adds some value. However, the baseline is 3 since schema already provides meaning, and the description does not significantly expand parameter semantics.
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 clearly specifies the tool validates a Belgian National Register Number, naming it in both languages (Rijksregisternummer / Numéro de Registre National) and mentions specific checks (11-digit form, mod-97 with born-from-2000 rule). This distinguishes it from sibling validators for other countries.
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 explicitly instructs to use this tool before relying on the number, providing clear context. It also states it validates structure only, which sets expectations. However, it does not explicitly exclude other scenarios or mention when not to use it, missing some depth.
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.