VAT Validator MCP
Server Details
Validate EU, UK, AU VAT numbers for AI agents. EU ViDA e-invoicing compliance.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- OjasKord/vat-validator-mcp
- GitHub Stars
- 0
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Tool Definition Quality
Average 4.7/5 across 2 of 2 tools scored.
The two tools have completely distinct purposes: one validates VAT numbers, the other retrieves VAT rates. There is zero ambiguity about which tool to use for a given task.
Both tools follow a consistent verb_noun pattern in snake_case (get_vat_rates, validate_vat), making their function immediately clear.
With only two tools, the set is minimal but well-scoped for a focused VAT validation and rate lookup service. It could benefit from additional tools (e.g., rate history), but the count is appropriate for its core purpose.
The set covers the two primary VAT operations: validation and rate retrieval. Minor gaps exist, such as no support for historical rates or bulk validation, but the essential workflows are complete.
Available Tools
2 toolsget_vat_ratesAInspect
Retrieves current VAT rates for a jurisdiction. Call this BEFORE calculating any invoice total or approving any VAT amount -- or immediately after validate_vat passing the country_code from that response. Use this when your agent needs to verify that the VAT rate on a supplier invoice matches the current official rate for that country before authorising payment. Returns current standard and reduced VAT rates for the jurisdiction. An agentic payment workflow that approves an invoice with an incorrect VAT rate creates a compounding compliance gap across every settled payment in that run -- VAT rates change without notice and cannot be sourced from training data. If the rate on the invoice differs from the rate returned here, do not approve payment.
| Name | Required | Description | Default |
|---|---|---|---|
| country_code | No | ISO 2-letter code e.g. DE, FR, GB. Omit for all countries. |
Output Schema
| Name | Required | Description |
|---|---|---|
| note | No | |
| rates | No | Present only when country_code is omitted -- full rate table for all supported jurisdictions |
| country | No | |
| reduced | No | Reduced VAT rates as percentages, if any apply |
| standard | No | Standard VAT rate as a percentage |
| checked_at | Yes | |
| source_url | Yes | |
| _disclaimer | No | |
| agent_action | Yes | |
| country_code | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description fully informs about the tool's behavior: it retrieves current rates, notes they change without notice, and warns about compliance gaps if rates are incorrect. It is clearly a read-only operation.
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 thorough but slightly verbose with the compliance gap explanation. However, it is well-structured with a logical flow: purpose, usage, return, rationale, warning. Every sentence adds value.
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 that an output schema exists (not shown), the description covers all necessary context: what it does, when to use, relationship to sibling tool, and critical warning. It is complete for an agent to invoke correctly.
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% for the single parameter, and the schema already includes the description. The tool description does not add new semantic meaning beyond the schema, but it is consistent.
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 states the tool retrieves current VAT rates for a jurisdiction, specifying the verb 'Retrieves' and resource 'VAT rates'. It distinguishes from sibling tool validate_vat by mentioning its role in the workflow after validation.
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?
Provides explicit when to call: before calculating invoice totals or approving VAT amounts, and immediately after validate_vat. Also gives a clear when-not-to-use scenario: if the rate differs from the on-invoice rate, do not approve payment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_vatAInspect
Validates a VAT number against live government registries. Call this BEFORE submitting any B2B payment via an agentic payment rail -- at the moment a VAT number appears on a supplier invoice and Stripe MPP, Alipay AI Pay, or Shopify UCP has not yet been called -- and BEFORE submitting any structured invoice under e-invoicing mandates now active or imminent: Belgium B2B (active Jan 2026), France B2B (Sep 2026), Poland KSeF (Feb 2026), AU GST digital reporting (ongoing). Use this when a supplier invoice carries a VAT number and your agent must confirm it is registered to the correct entity before approving payment or submitting a mandate-compliant e-invoice. Validates against EU VIES (ec.europa.eu, 27 member states) and AU ABR (abr.business.gov.au) live registries. Returns PROCEED / VERIFY_MANUALLY / HOLD verdict with fraud risk score 0-100 and name-match check. A settled B2B payment against an invalid or mismatched VAT number creates unrecoverable tax liability -- no agentic rail reverses a cleared cross-border transfer; an e-invoice submitted with an invalid VAT number is rejected at the mandate platform, halting the payables workflow. Pass the country_code from this response to get_vat_rates. One call, machine-ready verdict, no further analysis needed.
| Name | Required | Description | Default |
|---|---|---|---|
| vat_number | Yes | VAT number with country prefix. EU: DE123456789. AU: ABN12345678901. | |
| invoice_amount | No | Invoice amount in local currency — used in fraud risk weighting. | |
| invoice_company_name | No | Company name as it appears on the invoice — if provided, cross-checks against registry and flags mismatches. |
Output Schema
| Name | Required | Description |
|---|---|---|
| valid | Yes | Whether the VAT number is currently registered and active per the source registry |
| address | No | |
| summary | No | |
| checked_at | Yes | |
| name_match | No | |
| source_url | Yes | |
| vat_number | Yes | |
| _disclaimer | No | |
| agent_action | Yes | Machine-readable verdict |
| company_name | No | |
| jurisdiction | Yes | |
| fraud_signals | No | |
| recommendation | No | |
| fraud_risk_level | No | |
| fraud_risk_score | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description fully carries burden. Discloses validation against live registries (EU VIES, UK HMRC, AU ABR), return verdict (PROCEED/VERIFY_MANUALLY/HOLD) with fraud risk score and name-match check, and consequences of invalid number.
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?
Description is somewhat long but well-structured: opens with core action, then usage context, registries, return value, consequences. Every sentence adds information, though could be slightly more concise.
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?
Covers all aspects: purpose, usage timing, registries, return value, consequences, and links to sibling tool. No missing context given the complexity of the tool.
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%, but description adds value by explaining the purpose of each parameter: invoice_amount for fraud risk weighting, invoice_company_name for cross-check, and gives examples for vat_number format.
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?
Clearly states 'Validates a VAT number against live government registries', uses specific verb and resource, and distinguishes from sibling 'get_vat_rates' by mentioning passing country_code.
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?
Provides explicit when-to-use: before B2B payment via agentic rail, before submitting e-invoice under mandates. Also gives timing context and mentions alternative get_vat_rates.
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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