Retrieve current VAT rates for any European country (EU-27, EEA, UK, Switzerland, non-EU Europe) with category-driven breakdown of standard, reduced, super-reduced, zero-rated, and exempt categories plus registration thresholds and territorial exceptions. Premium API. Cost: $0.25 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| lookup | Yes | Country name, ISO code, city, or address |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says 'retrieve' implying a read operation, but annotations set destructiveHint=true, a contradiction. It mentions cost but does not clarify the destructive nature.
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 concise with three front-loaded sentences, each adding value: purpose, premium claim, and cost.
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?
The description covers the main use case and output details adequately for a single-parameter tool without output schema, though response format is omitted.
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?
The description adds meaning beyond the schema by specifying acceptable lookups (European countries) and output content (categories, thresholds), enriching the single parameter's context.
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 European countries, specifying the scope and output details. It distinguishes from sibling tools by focusing on VAT rates for Europe.
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 implies usage when European VAT rates are needed but does not explicitly compare to alternatives or state when not to use 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 target distinct data operations (matching, standardization, enrichment) with clear descriptions. Minor overlaps exist, e.g., address_match_advanced vs global_address_match, but descriptions differentiate them.
All tools follow a consistent 'interzoid_descriptive_function' pattern in snake_case, making it easy to predict purpose from the name.
58 tools is high for a single server, exceeding the typical 3-15 range. While each serves a specific data enrichment function, the quantity may overwhelm agents without clear categorization.
The tool surface covers a broad domain including address, company, person, and financial data. Minor gaps exist (e.g., no reverse IP lookup, limited social media coverage), but core data needs are well-addressed.
