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pranav-v-k

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 have clearly distinct purposes: one for arithmetic calculations and one for temperature conversion. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow a consistent verb-based pattern: 'calculate' and 'convert_temperature'. Though one is a single verb and the other is verb_noun, the style is coherent and predictable.

    Tool Count4/5

    With only two tools, the server is minimal but appropriate for a focused calculator MCP server. It feels slightly thin but not unreasonable.

    Completeness3/5

    The server covers basic arithmetic and temperature conversion, but lacks other common calculator features such as advanced math functions or general unit conversion. The coverage is adequate for a narrow calculator domain but has notable gaps.

  • Average 3.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 11 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

  • Behavior2/5

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

    No annotations are present, so the description carries the full burden of behavioral disclosure. It does not mention side effects, error handling (e.g., division by zero), return format, or purity. The phrase 'basic arithmetic calculations' simply restates the tool's function without adding 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.

    Conciseness5/5

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

    The description is a single, front-loaded sentence with no unnecessary words. It is concise and to the point.

    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?

    Given the tool's simplicity and full schema coverage, the description is minimally viable but lacks details about return values and edge-case behavior. With no output schema, the description could reasonably mention the result, but it does not.

    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 provides full descriptions for all three parameters (a, b, operation), including an enum for operation. The description adds no additional semantic information, so it is adequate but relies entirely on the schema.

    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 uses a clear verb ('perform') and resource ('basic arithmetic calculations'), making its purpose obvious. It does not explicitly differentiate from sibling 'convert_temperature' beyond the word 'basic', but the intent is clear.

    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?

    No guidance is provided on when to use this tool versus the sibling 'convert_temperature'. The description does not mention exclusions or alternative tools, so the agent receives no context for tool selection.

    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?

    With no annotations provided, the description must carry the full burden of behavioral disclosure. It claims 'direct input' is supported, but the schema requires file_name, file_type, and file_content, making file upload a mandatory path. This mismatch is misleading and not addressed. The description also does not disclose output format, default behavior, or error conditions.

    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 short and front-loaded, with two sentences that state the core purpose and supported units. No filler is present, earning high marks for conciseness. However, key behavioral details are missing, so it is not a perfect 5.

    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?

    Given six parameters, required file fields, no output schema, and no annotations, the description is under-specified. It fails to explain the two input modes, the mandatory nature of file parameters, how to choose between modes, or what the result looks like. A user or agent would have difficulty invoking this tool correctly based solely on the description.

    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?

    Schema description coverage is 100%, so the schema already documents all parameters, including enums for units. The description adds no further parameter-level meaning and does not clarify how file_content, value, or from_unit/to_unit relate to the two claimed input modes. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

    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 converts temperature units and identifies the supported units (Celsius and Fahrenheit). It distinguishes itself from the generic sibling 'calculate' by naming the specific resource and operation. However, it is slightly ambiguous about whether it converts file content, direct numeric input, or both.

    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 gives context for when to use the tool: when converting temperatures, either from file content or direct input. It does not explicitly mention alternatives or exclusions, such as using 'calculate' for other conversions or math. The guidance is present but not thorough.

    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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