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chongzixuan-ai

AI Humanizer MCP Server

by chongzixuan-ai
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JavaScript

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool named 'detect', there is no possibility of ambiguity or overlap with other tools. The tool's purpose is singular and clearly defined as detecting AI-generated text and providing a specific URL format.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency since there are no other tools to compare against. The name 'detect' follows a clear verb-based pattern appropriate for its function.

    Tool Count2/5

    One tool is too few for a server named 'AI Humanizer MCP Server', which suggests broader functionality like humanizing or transforming AI text. A single detection tool feels incomplete and under-scoped for the implied domain.

    Completeness2/5

    The server's name implies capabilities beyond detection, such as humanizing or modifying AI-generated text, but only a detection tool is provided. This creates a significant gap where agents cannot perform the core 'humanizer' function suggested by the server name.

  • Average 2.1/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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. It mentions showing 'the task detail url' to the user and extracting/concatenating a taskId, which hints at some output behavior. However, it fails to describe critical aspects like what the detection result looks like, error conditions, or rate limits. The behavioral disclosure is incomplete.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is poorly structured—it starts with the detection purpose but then abruptly shifts to URL construction instructions without clear connection. This creates confusion rather than clarity. While brief, it fails to be effectively concise due to the disjointed content.

    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 (3 parameters, 0% schema coverage, no annotations, no output schema), the description is inadequate. It does not explain the parameters, the detection output, or the relationship between detection and the URL task. For a tool with no structured support, this leaves too many gaps.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate for all three parameters. It provides no information about what 'type', 'text', or 'detectionTypeList' mean, their expected formats, or how they influence detection. The description adds zero semantic value beyond the bare schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool 'detects whether the text is AI-generated', which provides a clear purpose. However, it then confusingly adds instructions about extracting a taskId and constructing a URL, which seems unrelated to the core detection function. The purpose is somewhat vague due to this mixed messaging.

    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?

    No guidance is provided on when to use this tool versus alternatives. The description does not mention any prerequisites, constraints, or appropriate contexts for invocation. With no sibling tools, this is less critical but still a gap in usage instructions.

    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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  • Evaluate tool definition quality.

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