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Vertisky

Toolalize MCP

by Vertisky

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: convert_units handles general unit categories, convert_currency handles live exchange rates, and list_units provides discovery. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: convert_units, convert_currency, list_units. The style is uniform and intuitive.

    Tool Count5/5

    Three tools is perfectly scoped for a conversion server: one for discovery, one for general conversions, and one for currency. No unnecessary tools.

    Completeness5/5

    The surface covers the full lifecycle: discover available units, convert between units, and convert currencies. There are no obvious dead ends or missing operations.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

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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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It discloses that units resolve by id or symbol and that conversions are restricted to the same category, which are useful behavioral details. However, it does not describe the return format, error handling, or edge cases like ambiguous units, leaving some gaps.

    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, consisting of two sentences that each serve a purpose: the first defines the tool's action, and the second clarifies unit formats and directs to an alternative for currency. There is no redundant or irrelevant information.

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

    Completeness3/5

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

    For a tool with no output schema and no annotations, the description provides essential information but not full context. It does not describe the return value or how to enumerate available units (e.g., via list_units), and could mention behavior for mismatched categories. Nevertheless, it covers the core conversion use case sufficiently for an agent.

    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 covers 75% of parameters, and the description adds value by explaining how units are specified ('by id or symbol') with examples ('cm' or 'centimeter'). This enriches the meaning of 'from' and 'to' beyond the basic schema descriptions, and the 'same category' phrasing clarifies the optional 'category' parameter.

    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 converts numeric values between units of the same category, with specific verbs and resources. It also distinguishes from the sibling convert_currency by explicitly redirecting currency conversions, making its scope unmistakable.

    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 an explicit alternative for currency conversions ('For currency use convert_currency'), which helps with when-to-use guidance. It does not mention list_units for discovering available units, but the context is clear for non-currency conversions.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the burden of behavioral disclosure. It states that exchange rates are live and refreshed every 6 hours, which is a useful behavioral detail. However, it does not mention potential errors, network dependencies, or output specifics, leaving some transparency gaps.

    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 brief and well-structured: three short sentences, with the main purpose front-loaded. Each sentence provides distinct value (purpose, input format, usage guidance) with no redundancy.

    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 three-parameter tool with no output schema, the description covers the core functionality, input requirements, and usage context. It doesn't explicitly describe the return format, but 'convert' implies the converted amount, which is likely sufficient. Slightly more detail on output could improve completeness.

    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 provides descriptions for 'from' and 'to' but not for 'value'. The description clarifies that the tool converts an amount, implying 'value' is the monetary amount. It also reinforces the ISO 4217 code format for currency parameters, adding value beyond the schema.

    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 converts an amount between currencies using live exchange rates. It distinguishes from sibling tools (convert_units) by explicitly mentioning currency and live rates. The verb 'Convert' is specific and matches the tool name.

    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 explicitly says 'Use this instead of estimating rates from memory,' providing clear guidance on when to use the tool (when live rates are needed) and when not to (avoid manual estimation). It doesn't explicitly mention sibling tools, but the context makes the intended use clear.

    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 full burden. It clearly describes a read-only listing operation with no side effects, and adds useful context about the returned fields. For such a simple tool, this is sufficient disclosure.

    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 exactly two sentences with no filler. It front-loads the primary purpose and then adds a concise usage note, earning every word.

    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 (no parameters, no output schema), the description is complete: it states what the tool returns, when to use it, and how it relates to the sibling tool convert_units. Nothing important is missing for an agent to select and invoke it correctly.

    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 tool has zero parameters, so the description does not need to explain them. The schema already reflects this with an empty properties object, and the baseline for 0-parameter tools is 4. The description adds no parameter info because none is needed.

    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 uses the specific verb 'List' and clearly identifies the resource as 'every conversion category with its units', including the returned fields (id, symbol, name). It also distinguishes from the sibling tool by noting it should be called before convert_units.

    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?

    The description explicitly states when to use the tool: 'Call this to discover the vocabulary before convert_units'. This names the alternative tool and provides clear workflow context, satisfying the highest bar for usage guidance.

    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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