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pranav-v-k

NitroStack Calculator MCP Server

by pranav-v-k
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TypeScript

Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are clearly distinct: calculate handles arithmetic operations, while convert_temperature handles temperature unit conversion. There is no overlap in their purposes, so an agent can easily select the appropriate tool.

    Naming Consistency4/5

    Both names use verbs (calculate, convert), but 'calculate' lacks a noun object while 'convert_temperature' follows a verb_noun pattern. This is a minor inconsistency but the overall style is straightforward and readable.

    Tool Count3/5

    With only 2 tools, the server feels thin for a calculator-focused MCP. The tools cover two distinct capabilities, but the count is on the low end of acceptable and would benefit from additional operations or a broader scope.

    Completeness4/5

    The calculate tool covers basic arithmetic, and convert_temperature handles Celsius/Fahrenheit but misses Kelvin. For the server's stated purpose, the core operations are present, though a few common unit conversions are absent. Agents can work around these minor gaps.

  • Average 3.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 3 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

  • Behavior1/5

    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 does not mention edge cases like division by zero, return format, or side effects. The description is purely functional and omits all behavioral context.

    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 a single concise sentence with no redundant wording or filler. It earns its place by stating the core purpose without unnecessary detail.

    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?

    The tool is simple and the schema is rich, but the description lacks information about return values, error handling, or edge cases like division by zero. For an arithmetic tool, this is a notable gap, making the description adequate but incomplete.

    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 input schema has 100% coverage: all three parameters have descriptions, and operation has an enum. The description adds no additional parameter semantics, so the baseline of 3 applies as the schema fully documents the parameters.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool performs basic arithmetic calculations, and the schema enumerates specific operations (add, subtract, multiply, divide). It distinguishes from the sibling tool convert_temperature, which handles temperature conversion, though 'basic arithmetic' is somewhat generic.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. It simply states the function, leaving the agent to infer usage from the name and schema.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries full responsibility for behavioral disclosure. It mentions that conversion can be based on file content or direct input, but fails to explain that file_name, file_type, and file_content are required even for direct input, nor does it clarify how file content is processed or what happens with the data. This leaves significant behavioral ambiguity.

    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 front-load the main action and then add unit support details. Every word contributes to understanding the tool's purpose, making it highly efficient.

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

    Completeness2/5

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

    Despite having 6 parameters with no output schema and no annotations, the description is minimal. It does not explain the return format, error cases, why file parameters are required, or how the direct input mode interacts with the required file fields. This is insufficient for a tool with this complexity and dual-mode operation.

    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 has 100% coverage for parameter descriptions, so the baseline is 3. The description adds semantic value by grouping parameters into two modes: file-based (file_name, file_type, file_content) and direct input (value, from_unit, to_unit). This extra context helps the agent understand how the parameters relate to each other 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 uses a specific verb ('Convert') and resource ('temperature units'), and distinguishes itself from the sibling tool 'calculate' by focusing on temperature conversion. It also states the supported units (Celsius and Fahrenheit), making the tool's function clear and unambiguous.

    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 when to use the tool ('Convert temperature units based on file content or direct input') but does not explicitly state when not to use it or mention alternatives like 'calculate'. It gives context about two modes but lacks clear usage boundaries or exclusions.

    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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