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koppe-pan

mcp-svg-marp

by koppe-pan
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: one analyzes an SVG to suggest a Marp structure, the other converts an SVG to a Marp presentation. There is no functional overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case verb_noun pattern: convert_svg_to_marp and analyze_svg. The naming is predictable and clearly indicates the action and target.

    Tool Count3/5

    With only 2 tools, the server feels slightly thin for a conversion-oriented domain. However, the tools are tightly focused and cover the primary workflow, making the count borderline but defensible.

    Completeness4/5

    The core workflow of analyzing an SVG for structure and then converting it to Marp is fully covered. Minor gaps exist, such as no tool for managing multiple conversions or editing Marp output, but these are not dead ends for the main purpose.

  • 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
    • 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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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 must carry the full behavioral burden. It conveys a read-only intent via 'Analyze' but does not disclose what the output looks like, whether it modifies anything, or any limitations. The minimal wording fails to give the agent a clear picture of the tool's runtime behavior.

    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 that avoids redundancy. It directly states the tool's purpose and output, with no wasted words.

    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?

    There is no output schema, so the description should at least hint at the returning structure or format, but it does not. Additionally, the sibling tool's existence suggests a workflow context that is unaddressed, leaving the description incomplete for practical use.

    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; the only parameter, svgPath, is described as 'Path to the SVG file to analyze', which is sufficient. The description does not add additional parameter-level detail, but baseline 3 applies since the schema fully documents the parameter.

    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 states a specific action ('Analyze an SVG file') and the intended output ('suggest Marp presentation structure'), making the tool's purpose immediately clear. It distinguishes itself from the sibling tool convert_svg_to_marp by emphasizing the suggestion/analysis aspect rather than direct conversion.

    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 explicit guidance on when to use this tool versus the sibling convert_svg_to_marp. It only states what it does, leaving the when-to-use decision entirely implicit.

    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, and the description does not disclose any side effects such as file creation, overwriting behavior, or required permissions. It also omits the default output path behavior, leaving the agent without crucial operational details.

    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 that immediately states the tool's purpose without any unnecessary words or repetition.

    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 description is minimal and fails to mention conversion options, the optional outputPath default, or how it relates to the sibling tool. While the schema provides parameter details, the description lacks operational context needed for an agent to fully understand the tool's behavior.

    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 fully documents all parameters. The description adds no additional parameter semantics, meeting the baseline expectation for coverage.

    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 tool converts an SVG file to a Marp presentation, using a specific verb and resource. The verb 'convert' is distinct from the sibling tool 'analyze_svg', making the purpose unambiguous and well-differentiated.

    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?

    There is no guidance on when to use this tool versus the sibling 'analyze_svg', nor any mention of prerequisites or conditions. The description only provides the basic action without usage context.

    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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