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vedangps

NitroStack Hackathon Starter

by vedangps
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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 handles general arithmetic and the other handles temperature conversion. There is no overlap or ambiguity between them, so an agent can easily select the correct tool.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: 'calculate' and 'convert_temperature'. Both use lowercase with underscores, maintaining a predictable naming convention across the set.

    Tool Count3/5

    With only two tools, the server feels very thin for a 'Hackathon Starter' name, which suggests a broader utility toolkit. This makes the count borderline, though it is still functional for basic needs.

    Completeness2/5

    The server appears to aim at providing math utilities, but it only offers basic arithmetic and temperature conversion. There are significant gaps such as length/weight conversions, currency conversion, or more advanced math functions, leaving the surface severely incomplete for a general-purpose starter kit.

  • Average 3.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 is passing

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It does not mention edge cases (e.g., division by zero), return format, or any side effects. The description is purely functional and lacks transparency about behavior beyond the operation itself.

    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 a single short sentence with no wasted words. It is appropriately minimal for a simple tool, though it could have included behavioral details without harming conciseness.

    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?

    The tool is simple, but the description omits crucial context such as the return value and edge-case behavior. Since there is no output schema, the description should explain what the caller receives, and it does not. The absence of warnings about division by zero or other pitfalls makes it 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?

    Schema description coverage is 100%, with all three parameters (a, b, operation) already described in the schema. The description adds no additional semantic meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

    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 'Perform basic arithmetic calculations' clearly states the tool's function with a specific verb and resource. It implicitly distinguishes from sibling convert_temperature by focusing on arithmetic rather than conversion, though it does not explicitly name the operations.

    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 like convert_temperature. There are no explicit use cases, exclusions, or context hints beyond the tool name, leaving the agent to infer applicability.

    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 must disclose behavioral traits. It does not disclose the return format, whether it modifies state, the relationship between file content and direct input, or any prerequisites. The phrase 'based on file content or direct input' is ambiguous without explaining precedence or requirements given the schema's required file parameters.

    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 two sentences, front-loads the primary action, and contains no redundant or filler content. Every phrase adds information about the tool's scope.

    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?

    The tool has six parameters, no output schema, and no annotations. The description does not explain the required file parameters, how to invoke the tool correctly, or what the return value is, leaving significant gaps for a tool with this complexity.

    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 baseline is 3. The description adds the concept of 'file content or direct input', which provides some context beyond the schema, but it does not clarify parameter combinations or precedence, so it does not meaningfully exceed the baseline.

    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 verb 'convert' and the resource 'temperature units', specifically supporting Celsius and Fahrenheit. It distinguishes from the sibling tool 'calculate' by specifying the domain of temperature conversion.

    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 usage for temperature conversion but does not explicitly state when to use this tool over 'calculate' or provide exclusions. It mentions 'based on file content or direct input' as usage context but lacks explicit guidance on choosing between the two input modes or alternative tools.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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