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mcp-revenue-empire — Japan public-data ledgers

fx_tax_vat_calc

Compute VAT for an amount in a given country from built-in VAT rate tables: net, VAT and gross, standard or reduced rate, treating the amount as VAT-inclusive or exclusive. Pure; price 0.0 (free).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountYesAmount to compute VAT on
countryYesISO 3166 country code with a VAT rate on file
rateTypeNoWhich VAT rate to apply (default: standard)
amountIncludesVatNoTreat amount as VAT-inclusive (gross) rather than net (default: false)

TDQS

A3.8/5.0
Behavior3/5

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

Without annotations, the description partially compensates by noting it's 'Pure; price 0.0 (free),' implying no side effects or cost. It also mentions using built-in VAT rate tables, suggesting offline computation, but it doesn't cover error cases (e.g., unsupported country) or rounding 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 packs all key information into two sentences with no filler. The first sentence front-loads the core function, and the second adds a critical behavioral note about cost and purity.

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

Completeness4/5

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

For a simple calculation tool with no output schema, the description covers inputs, rate types, and output concepts (net, VAT, gross). It lacks explicit mention of unsupported country handling or response format, but these are minor gaps given the tool's simplicity.

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?

The schema covers all four parameters at 100%, so the baseline is 3. The description echoes parameter choices (standard/reduced, inclusive/exclusive) but adds no new syntax or formats beyond what the schema already documents.

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 identifies the tool's function: computing VAT for a given amount and country. It explicitly mentions net/VAT/gross outputs and distinguishes from sibling tools like fx_tax_convert or fx_tax_sales_tax by focusing on VAT calculation specifically.

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

Usage Guidelines3/5

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

The description implies usage for VAT calculations but doesn't explicitly state when to choose it over fx_tax_sales_tax or fx_tax_convert. No exclusions or alternatives are mentioned, leaving the agent to infer based on the 'VAT' keyword.

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

B3.1/5.0
Disambiguation4/5

Most tools are clearly distinguished by domain prefixes (e.g., bid_watch, grant_watch) and specific action verbs. However, the high number of similarly structured watch tools could still cause confusion, though descriptions clarify exact purposes.

Naming Consistency5/5

Every tool follows a consistent `domain_subdomain_action` pattern with underscores, e.g., `agent_audit_query`, `bid_watch_search`. Even long names like `commerce_catalog_agent_readiness_score` adhere to this structure.

Tool Count2/5

With 147 tools, the server is far too broad, covering weather, carbon estimates, domain intel, and more—well beyond its stated 'Japan public-data ledgers' scope. This sheer volume overwhelms agents and dilutes focus.

Completeness3/5

The server offers many read-only tools for Japanese public data (bids, grants, licenses, etc.), but lacks create/update/delete operations for those domains. Additionally, numerous unrelated tools (e.g., carbon estimates, weather) feel tacked on, leaving gaps in core coverage.

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