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yehyaabk

Tax MCP Agent

by yehyaabk
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of ambiguity. The tool's purpose is clearly defined as VAT calculation.

    Naming Consistency5/5

    The single tool name 'calculate_tax' follows a clear verb_noun pattern, which is internally consistent and predictable.

    Tool Count3/5

    A single tool feels thin for an agent labeled 'Tax MCP Agent'. While it serves a focused purpose, the name suggests a broader scope that could use additional utilities like rate lookup or country validation.

    Completeness4/5

    The tool covers core VAT calculation for a given country and price, but lacks a way to discover supported countries or retrieve tax rates directly. These are minor gaps that agents can work around via the error messages.

  • Average 4.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 6 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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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 fully discloses the tool's behavior: it calculates VAT and total price, returns a structured dict with specific keys, and returns an error dict for unknown countries or invalid input. This covers error handling and return format, which are key behavioral aspects.

    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 is concise and well-structured with clear sections for args and returns. It front-loads the primary purpose in the first sentence, and every subsequent line adds necessary detail without redundancy. The length is appropriate for the tool's complexity.

    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 simplicity, two parameters, and lack of output schema, the description is complete: it explains inputs, validation rules, return structure, and error behavior. There are no gaps that would leave an agent uncertain about how to invoke or interpret the tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema only lists parameter names and types (country: string, price: number) with no descriptions. The description adds critical semantics: country is case-insensitive, price is excluding VAT and must be positive. Since schema coverage is 0%, the description fully compensates and enriches both parameters.

    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 function's purpose: 'Calculate the VAT amount and total price for a given country and price.' This is a specific verb+resource statement. Although there are no sibling tools to differentiate, the description unambiguously defines what the tool does without tautology.

    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 specifies the required inputs ('for a given country and price') and notes that the price must be positive. It does not explicitly mention alternative tools, but since there are no sibling tools, this is not a deficiency. The usage context is clear and sufficient for a calculation tool.

    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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  • Evaluate tool definition quality.

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