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BACH-AI-Tools

AI Content Detector

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

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, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined for detecting AI-generated content from specific models.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tool names to compare it against for patterns or conventions. The name is descriptive and follows a clear structure.

    Tool Count2/5

    One tool is too few for a server named 'AI Content Detector', as it suggests a limited scope that might not cover related operations like analyzing different AI models, providing confidence scores, or handling batch detection. This could lead to incomplete functionality for agents.

    Completeness2/5

    The tool surface is severely incomplete for the domain of AI content detection; it only detects content from three specific models (ChatGPT, GPT4, Gemini), missing obvious gaps such as detection for other AI models, detailed analysis features, or integration with broader content verification workflows.

  • Average 2.9/5 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 is passing

  • 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. It states the tool detects content but doesn't explain how it behaves (e.g., input format, output type, accuracy, limitations, or any side effects like rate limits or authentication needs). This leaves significant gaps in understanding the tool's operation.

    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 extremely concise—a single sentence with no wasted words. It's front-loaded with the core purpose, making it easy to scan. Every word earns its place by specifying the AI models involved, though it could be more informative.

    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's complexity (detection of AI-generated content) and lack of annotations or output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., confidence scores, classifications), how to interpret results, or any prerequisites. This leaves the agent with insufficient information to use the tool effectively.

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

    Parameters4/5

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

    The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, but since there are no parameters, this is acceptable. The baseline for 0 parameters is 4, as the description doesn't need to compensate for missing parameter info.

    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's purpose is to detect content from specific AI models (ChatGPT, GPT4, Gemini), which is clear but somewhat vague. It doesn't specify what 'detect' means operationally (e.g., identify authorship, classify text, analyze patterns) or what resource it operates on (text input, files, etc.). No sibling tools exist to differentiate from, but the purpose could be more specific.

    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, such as scenarios for detection (e.g., academic integrity, content moderation) or alternatives. With no sibling tools, there's no need to distinguish between them, but the lack of any usage context leaves the agent without direction on applicability.

    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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