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AI Content Detector

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Ai Content Detector Ai Gpt MCP Server

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用于访问 Ai Content Detector Ai Gpt API 的 MCP 服务器。

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Related MCP server: AI Content Detector MCP Server

简介

这是一个 MCP 服务器，用于访问 Ai Content Detector Ai Gpt API。

  • PyPI 包名: bach-ai_content_detector_ai_gpt

  • 版本: 1.0.0

  • 传输协议: stdio

安装

从 PyPI 安装:

pip install bach-ai_content_detector_ai_gpt

从源码安装:

pip install -e .

运行

方式 1: 使用 uvx（推荐，无需安装）

# 运行（uvx 会自动安装并运行）
uvx --from bach-ai_content_detector_ai_gpt bach_ai_content_detector_ai_gpt

# 或指定版本
uvx --from bach-ai_content_detector_ai_gpt@latest bach_ai_content_detector_ai_gpt

方式 2: 直接运行（开发模式）

python server.py

方式 3: 安装后作为命令运行

# 安装
pip install bach-ai_content_detector_ai_gpt

# 运行（命令名使用下划线）
bach_ai_content_detector_ai_gpt

配置

API 认证

此 API 需要认证。请设置环境变量:

export API_KEY="your_api_key_here"

环境变量

变量名

说明

必需

API_KEY

API 密钥

是

PORT

不适用

否

HOST

不适用

否

在 Cursor 中使用

编辑 Cursor MCP 配置文件 ~/.cursor/mcp.json:

{
  "mcpServers": {
    "bach-ai_content_detector_ai_gpt": {
      "command": "uvx",
      "args": ["--from", "bach-ai_content_detector_ai_gpt", "bach_ai_content_detector_ai_gpt"],
      "env": {
        "API_KEY": "your_api_key_here"
      }
    }
  }
}

在 Claude Desktop 中使用

编辑 Claude Desktop 配置文件 claude_desktop_config.json:

{
  "mcpServers": {
    "bach-ai_content_detector_ai_gpt": {
      "command": "uvx",
      "args": ["--from", "bach-ai_content_detector_ai_gpt", "bach_ai_content_detector_ai_gpt"],
      "env": {
        "API_KEY": "your_api_key_here"
      }
    }
  }
}

可用工具

此服务器提供以下工具:

chatgpt_gpt4_u0026_gemini_detect

Detect ChatGPT, GPT4 \u0026 Gemini Content.

端点: POST /api/detectText/

技术栈

  • 传输协议: stdio

  • HTTP 客户端: httpx

许可证

MIT License - 详见 LICENSE 文件。

开发

此服务器由 API-to-MCP 工具生成。

版本: 1.0.0

Available Tools

1 tool
chatgpt_gpt4_u0026_gemini_detectC

Detect ChatGPT, GPT4 \u0026 Gemini Content.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

C2.9/5.0
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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedchatgpt_gpt4_u0026_gemini_detect
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 1 tool

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.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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