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happyteam-dx1

MCP Sample Server

by happyteam-dx1
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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: 'calculate' handles mathematical operations, while 'get_current_time' retrieves temporal information. There is no overlap in functionality, making it impossible for an agent to confuse them.

    Naming Consistency3/5

    The naming is mixed: 'calculate' uses a verb-only style, while 'get_current_time' follows a verb_noun pattern. Although both are readable, they lack a consistent convention, which could lead to minor confusion in a larger set.

    Tool Count2/5

    With only 2 tools, this server feels thin and under-scoped for a general-purpose 'MCP Sample Server'. It lacks coverage for common operations beyond basic math and time, suggesting it might be a minimal example rather than a fully functional server.

    Completeness2/5

    Given the server's name implies a sample or general utility scope, the toolset is severely incomplete. It misses obvious utilities like string manipulation, file operations, or data conversion, leaving significant gaps that would hinder agent workflows.

  • Average 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
    • 0 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

  • This repository is licensed under MIT License.

  • 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 provided, so the description carries the full burden of behavioral disclosure. The description only states what calculations it performs but doesn't disclose any behavioral traits like error handling (e.g., division by zero), precision, performance characteristics, or what the output looks like. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

    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 very concise - a single sentence in Japanese that efficiently states the purpose. It's front-loaded with the main function and includes parenthetical examples. There's no wasted text, though it could be slightly more structured if it included brief usage notes.

    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 the tool has no annotations and no output schema, the description is incomplete. While it states what calculations are performed, it doesn't cover important contextual aspects like return values, error conditions, or behavioral constraints. For a calculation tool with 3 parameters and no structured output information, the description should do more to help an agent understand how to use it effectively.

    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 schema description coverage is 100%, meaning all parameters are documented in the schema. The description doesn't add any parameter semantics beyond what's already in the schema (operation with enum values, a and b as numbers). It doesn't explain parameter relationships, constraints, or examples. With high schema coverage, the baseline is 3 even without additional param info in the description.

    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 what the tool does: '簡単な計算を実行します（加算、減算、乗算、除算）' translates to 'Performs simple calculations (addition, subtraction, multiplication, division).' This specifies the verb ('performs calculations') and the resource/scope ('simple calculations' with listed operations). It distinguishes from the sibling tool 'get_current_time' which is unrelated. However, it doesn't explicitly differentiate from potential calculation alternatives beyond listing 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. It doesn't mention any prerequisites, constraints, or scenarios where this tool is appropriate versus other calculation methods. The only sibling tool 'get_current_time' is completely unrelated, so no comparison is needed, but the description offers no usage context at all.

    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 the full burden of behavioral disclosure. While '取得します' (get) implies a read-only operation, the description doesn't explicitly state whether this requires permissions, has rate limits, or what the return format looks like (e.g., timestamp, formatted string). For a tool with zero annotation coverage, this is a significant gap in transparency.

    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, efficient sentence in Japanese that directly states the tool's purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

    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 low complexity (single optional parameter) and high schema coverage, the description is minimally adequate. However, with no annotations and no output schema, it doesn't fully compensate for missing behavioral details like return format or error conditions, keeping it at a baseline level.

    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 schema description coverage is 100%, with the single parameter 'timezone' fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, such as default behavior when timezone is omitted. With high schema coverage, 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 clearly states the tool's purpose as '現在の時刻を取得します' (Get the current time), which is a specific verb+resource combination. However, it doesn't explicitly differentiate from its sibling tool 'calculate', which might also handle time-related calculations, so it doesn't reach the highest score.

    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 the 'calculate' sibling tool. It doesn't mention any context, exclusions, or prerequisites for usage, leaving the agent to infer appropriate scenarios.

    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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