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sweet8413-spec

TypeScript MCP Server Boilerplate

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes with no overlap: one generates images from text prompts, while the other returns greetings based on name and language. An agent would never confuse these tools as they operate in entirely different domains.

    Naming Consistency3/5

    The naming is mixed but readable: 'generate-image' follows a verb-noun pattern, while 'greet' is just a verb. There's no consistent pattern across both tools, but the names are clear and descriptive enough to understand their functions.

    Tool Count2/5

    With only 2 tools, this server feels extremely thin for a 'TypeScript MCP Server Boilerplate' which suggests a broader utility scope. The tools provided (image generation and greeting) don't align well with typical boilerplate functionality like code generation or project setup.

    Completeness1/5

    For a server labeled as a 'TypeScript MCP Server Boilerplate', there are severe gaps: no tools for generating TypeScript code, setting up projects, configuring MCP servers, or any other boilerplate-related tasks. The existing tools feel like random examples rather than a coherent surface for the stated purpose.

  • 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
    • 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
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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. It only states the basic function ('generate an image') without mentioning any behavioral traits such as rate limits, authentication needs, output format, or potential side effects (e.g., resource usage, cost). For a generative tool with no annotation coverage, this leaves significant gaps in understanding how it behaves beyond the core action.

    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 that directly states the tool's purpose without any extraneous information. It is appropriately sized and front-loaded, making it easy to understand at a glance. Every word earns its place, contributing to clarity.

    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 complexity of an image generation tool, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., performance, limitations), output characteristics (e.g., image format, size), and usage context. The description alone is insufficient for an agent to fully understand how to invoke and interpret results from this tool.

    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 both parameters ('prompt' and 'num_inference_steps') well-documented in the schema. The description adds no additional meaning beyond what the schema provides, as it doesn't explain parameter roles or interactions. Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to.

    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: '텍스트 프롬프트로 이미지를 생성합니다' translates to 'Generate an image from a text prompt.' This specifies the verb ('generate') and resource ('image'), making the function unambiguous. However, it doesn't distinguish from the sibling tool 'greet', which appears unrelated, so differentiation isn't necessary but could be noted for completeness.

    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 states what the tool does but doesn't mention any prerequisites, constraints, or scenarios where it should be preferred over other methods. With no explicit usage context, the agent must infer usage from the purpose alone.

    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. It mentions that the tool returns a greeting but doesn't describe any behavioral traits such as error handling, response format, or potential side effects. For a tool with no annotations, 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 Korean that directly states the tool's function. It is front-loaded with the core purpose and uses no unnecessary words, making it highly concise and well-structured for quick understanding.

    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?

    Given the tool's low complexity (simple greeting function), 2 parameters with full schema coverage, and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the basic purpose adequately, though it lacks behavioral details that could enhance agent understanding in the absence of annotations.

    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 clear descriptions for both parameters (name and language) and an enum for language. The description adds minimal value beyond the schema, as it only implies that name and language are inputs without providing additional semantic context. Baseline 3 is appropriate given the high schema coverage.

    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: '입력하면 인사말을 반환합니다' (returns a greeting when given inputs). It specifies the verb (returns greeting) and resources (name and language). However, it doesn't explicitly differentiate from the sibling tool 'generate-image', which is unrelated but could benefit from clearer distinction in a broader context.

    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 states what the tool does but offers no context about scenarios, prerequisites, or comparisons to other tools. This leaves the agent without usage direction beyond the basic function.

    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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