validate_nif
Validate Portuguese NIF/NIPC tax identification number (mod-11 check digit).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| nif | Yes | Portuguese NIF or NIPC (9 digits) |
swiss-army
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the algorithm (mod-11 check digit) but omits critical behavioral details such as return type (true/false versus throwing an error), handling of invalid formats, or any side effects. The description is too sparse to fully inform an agent of the tool's runtime behavior.
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 a single, focused sentence that includes both the resource and the validation method. No unnecessary words or repetition, making it optimally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, but with no annotations and no output schema, the description should at least clarify the return behavior. It does not state whether the tool returns a boolean or raises an exception for invalid input. This gap prevents the description from being fully complete for an agent selecting and invoking the tool.
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 parameter 'nif' is already described as a 9-digit Portuguese NIF/NIPC. The description repeats this but adds no new meaning beyond mentioning the mod-11 check digit, which is more about behavior than parameter semantics. Baseline 3 is appropriate.
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 clearly states the tool validates Portuguese NIF/NIPC tax identification numbers using the mod-11 check digit. This specific verb+resource+scope distinguishes it from sibling validation tools like validate_iban and verify_eu_vat.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not mention when to use this tool versus alternatives, nor does it exclude specific cases. While the tool's purpose is obvious from the name, the description itself offers no explicit guidance on selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A3.5/5.0
Disambiguation5/5
Each tool has a distinct purpose and target resource or operation. While some tools are thematically related (e.g., detect_secrets and classify_gdpr both analyze text), their specific outputs and use cases are clearly separated by names and descriptions.
Naming Consistency4/5
Most tools follow a clear verb_noun pattern (convert_currency, generate_uuid, validate_iban), and the noun_to_noun conversion tools (csv_to_json, html_to_text) form a consistent sub-pattern. The mix of verb_noun and X_to_Y is understandable and predictable, though not uniform.
Tool Count3/5
23 tools is on the higher end for a utility server, feeling like a grab-bag of many unrelated functions. While each tool is simple and serves a purpose, the count exceeds the typical well-scoped range, making it heavier than ideal.
Completeness3/5
The tool coverage is broad but scattered with no clear domain focus. Obvious complementary utilities are missing, such as URL encoding/decoding, YAML conversion, or PDF generation. However, within each small category, core operations are present, so agents can work around gaps.