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MCP Typography Audit Server

by kobaltgit
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only a single tool in the set, there is no possibility of confusing it with another tool. Its purpose (render HTML, apply Knuth-Plass justification, return a layout audit screenshot) is uniquely identifiable.

    Naming Consistency5/5

    There is only one tool, so no naming pattern can be violated. The single name 'audit_typography' follows a clean verb_noun convention.

    Tool Count2/5

    A single tool is too thin for what appears to be an auditing domain — there is no way to configure rendering, retrieve detailed findings, or target specific elements. One monolithic tool that does render+justify+screenshot is under-scoped.

    Completeness2/5

    The surface offers one all-in-one operation with no companion tools for configuration, structured result retrieval, or targeted checks. Agents needing anything beyond a single screenshot audit (e.g. per-element diagnostics or custom width/font inputs) hit a dead end.

  • Average 3.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 5 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

  • Behavior3/5

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

    With no annotations, the description carries the full burden and it does disclose a useful processing pipeline (render, justify via Knuth-Plass, screenshot output). However, it omits important behavior: whether submitted HTML/scripts are executed, sandbox/permission requirements, latency, and what form the screenshot takes.

    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?

    A single front-loaded sentence that states the pipeline and the output with no filler. Every clause earns its place and the outcome (screenshot + audit) is stated first.

    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?

    There is no output schema, so the description must explain the return value; it states a screenshot with a layout audit is returned, but not the concrete form (URL, base64, image part) or contents of the audit. Combined with no annotations, the definition is adequate but leaves real gaps for an agent.

    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 html, lang, and width with examples. The description adds no parameter meaning beyond the schema, so the baseline of 3 applies.

    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 gives a concrete verb chain and resource: renders HTML, applies the Justif (Knuth-Plass) algorithm, and returns a screenshot with a layout audit. An agent can tell this is a typography/verstka auditing tool, though there are no siblings to differentiate it from.

    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 explicit guidance on when to use this tool, what prerequisites exist, or what alternatives it competes with. The description is entirely about internal processing rather than the conditions that select this tool.

    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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MCP Typography Audit Server MCP server – quality and maintenance score on Glama

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