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bartosz-kuc

honest-vies-mcp

Server Quality Checklist

75%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one validates a VAT number, the other lists supported country codes. There is no overlap or ambiguity in their functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (check_vat, list_supported_countries). The naming is predictable and uniform.

    Tool Count4/5

    With only two tools, the server is minimal but appropriately scoped for its narrow purpose of VIES VAT validation. It is slightly thin but each tool earns its place, so it is reasonable.

    Completeness5/5

    The tool surface fully covers the domain: checking VAT numbers (with optional consultation number for legal proof) and listing supported countries. No obvious gaps exist for the intended use case.

  • Average 4.7/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 10 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  • This server has been verified by its author.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior5/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the return behavior (valid/invalid, name/address if available), the combined vs. separate input forms, and the special consultation number mechanism for Polish JDG. It also warns about when VIES will not issue a consultation number, which is a critical behavioral nuance.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

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

    The description is moderately long but each sentence contributes information. It front-loads the core purpose and return value, then details the input forms and the special case. There is no filler or repetition, though it could be slightly trimmed without losing critical detail.

    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?

    Given the tool's complexity (5 parameters, two input modes, optional requester fields, a special legal-proof mechanism), the description covers all necessary aspects: input variants, return format, and the condition for obtaining a consultation number. Without an output schema, the description adequately explains what the agent can expect from the call.

    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?

    The schema description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema: it explains the relationship between requester_country and requester_vat, emphasizes the combined 'vat' parameter's precedence, and provides the Greek/Northern Ireland country code exceptions. This exceeds the baseline by adding practical usage semantics.

    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's function: checking an EU VAT number against the VIES database. It specifies the verb 'Check', the resource (EU VAT number), and the validation scope (VIES). It also distinguishes itself from the sibling tool (list_supported_countries) by focusing on validation rather than listing countries.

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

    Usage Guidelines4/5

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

    The description provides clear context on when to use the tool (to check VAT numbers) and even includes a specific use case (Polish JDG issuing 0% VAT invoices). However, it does not explicitly discuss alternatives or exclusions relative to list_supported_countries, though the purpose difference is implicit. Missing a direct 'when not to use' guidance, but the context is sufficient.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries the burden. It clearly describes a read-only listing operation with no side effects, and specifies the exact set of codes. It doesn't state the return format, but for a zero-parameter list tool the implication of returning an array of strings is strong enough. Slight gap in not explicitly stating it is safe/read-only, but it is entirely inferred from the verb 'List'.

    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?

    A single sentence front-loads the action, resource, and scope without any wasted words. Every element ('List', 'country codes', 'VIES', the list of codes) earns its place. No redundancy or filler.

    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 zero-parameter, no-output-schema tool, this description is complete. It tells the agent exactly what the tool returns (the set of supported codes) and the extent of that set. There is nothing an agent needs to know to call it correctly that is missing.

    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?

    There are zero parameters, so the baseline per rubric is 4. The description adds no parameter information because none is needed; the tool takes no input and simply returns a static list.

    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?

    States a specific verb ('List'), a specific resource ('country codes supported by VIES'), and the exact scope (27 EU member states plus XI). This unambiguously distinguishes it from the sibling check_vat, which performs a different operation.

    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?

    Although it doesn't explicitly name check_vat, the purpose is self-evident: use this tool when you need the set of supported country codes. There is no alternative for listing countries, so no exclusion is needed. The context makes the appropriate usage clear without ambiguity.

    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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