validate_vatEU VAT format validation with deterministic checksums (DE, IT, LU, PL, SI). $0.001 per call.
| Name | Required | Description | Default |
|---|---|---|---|
| vat | Yes | EU VAT number including country prefix (e.g. DE123456789, LU12345678) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal read-only, idempotent, non-destructive behavior. The description adds meaningful context by disclosing deterministic checksum support for specific countries and the per-call cost, which goes beyond annotation coverage.
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 a single dense sentence that front-loads the core purpose and includes useful behavioral and cost information without filler. Every word earns its place.
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 one-parameter validation tool, the description is mostly sufficient: it states the operation, supported countries, and cost. However, there is no output schema and the description does not disclose what the tool returns (e.g., boolean, verdict object, or error), which is a relevant gap for an agent invoking 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%, so the vat parameter is already fully described as an EU VAT number with country prefix and examples. The description adds the supported country checksum list but does not materially improve parameter 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 states a specific verb and resource: 'EU VAT format validation.' It is clear the tool validates EU VAT numbers, and the supported checksum details add specificity. However, it does not explicitly distinguish itself from sibling validation tools like validate_iban or validate_bic beyond the VAT resource being named.
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 clearly implies this tool is for validating EU VAT numbers and lists which country checksums are supported. It lacks explicit exclusions or references to alternative tools, but the context is clear enough for an agent to know when to select it.
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.
Most tools are cleanly separated by domain prefix (data_, security_, validate_, verify_) and describe distinct outputs. A couple of pairs like token_verdict vs verify_token or data_portfolio vs wallet_dossier could be confused until descriptions are read, but the descriptions resolve the ambiguity.
The majority of tools follow a predictable prefix_subject pattern, e.g. data_block, security_tls, validate_iban, verify_payment. A few outliers like html_to_markdown, text_diff, wallet_dossier, and token_verdict use different conventions, but the overall system remains readable.
20 tools is on the heavy side for the typical MCP server and sits in the 16–25 'feels heavy' zone. Most tools have a legitimate purpose, but the five validate_* identifier tools plus two generic utilities could feel like surface area bloat.
The server covers a broad and coherent read/validation domain: chain data, token safety, payments, security checks, and identifier validators. Minor gaps exist, such as lack of detailed historical transaction/activity data and no general network/domain security scan beyond email, TLS, and typosquat.