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Glama

Vat

by io.github.pipeworx-io

Validate Vat

validate_vat
Read-onlyIdempotent

Validate the FORMAT of a VAT registration number for the EU (+ UK/CH/NO), keyless & offline. Accepts the number with its country prefix ("DE123456789") or a national number plus country. Returns whether it matches the official country pattern. NOTE: format only — it does NOT confirm the VAT number is registered/active (use the EU VIES service for that).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vatYesA VAT number, e.g. "DE123456789" or "GB123456789".
countryNoOptional 2-letter country code (EL for Greece, GB for UK) if `vat` has no prefix.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "vat": "DE123456789"
+  },
+  {
+    "country": "FR",
+    "vat": "123456789"
+  }
+]
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds key behavioral details beyond annotations: 'keyless & offline', format-only validation, and that it does not confirm active registration. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose, then usage details and limitations. No irrelevant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple validation tool with no output schema, the description covers purpose, parameters, behavior, and limitations adequately. Return value (boolean match) is implied.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. Description adds value by explaining the two input patterns (prefix vs. national number + country) with examples, providing clarity beyond schema descriptions.

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 the FORMAT of a VAT registration number for EU+UK/CH/NO. It distinguishes from siblings by noting it does not confirm registration, directing to VIES service.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states two acceptable input formats (with prefix or separate country code) and clearly notes what the tool does NOT do (not a registration check), guiding when to use alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying catalog, and ask_pipeworx_beta is currently identical to ask_pipeworx. ai_visibility_check and scan_competitor_ai_presence also overlap, and entity_profile vs recent_changes cover similar ground. The descriptions are detailed, but an agent would frequently struggle to pick the right tool.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: some are verb-first (validate_vat, list_vat_formats, resolve_entity), some are noun-first (entity_profile, polymarket_edges, recent_alerts), and some are product-branded (ask_pipeworx, pipeworx_feedback). It's readable but does not follow a single predictable verb_noun convention.

Tool Count2/5

33 tools is well beyond the 25+ threshold for a coherent set, especially for a server named 'Vat' where only two tools (list_vat_formats, validate_vat) relate to the apparent purpose. The bulk forms a broad data-research platform that would be more appropriately split into separate VAT and Pipeworx servers.

Completeness4/5

For the actual data-research domain, coverage is strong: discovery, single lookup, grounded answers, deep multi-source research, entity resolution, comparisons, claim verification, memory, subscriptions, and prediction-market tooling are all present. The only clear gap is that the VAT-specific surface is minimal (format validation only, no registration/VIES check), but the overall functional surface is otherwise quite complete.

Resources