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GPTZero MCP Server

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@louis030195/gptzero-mcp

MCP (Model Context Protocol) server for GPTZero AI detection API. Detect AI-generated text directly from Claude, ChatGPT, or any LLM that supports MCP.

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Features

  • 🤖 Detect AI-generated text with confidence scores

  • 🌍 Multilingual support (French, Spanish)

  • 📊 Detailed probability breakdowns (AI/Human/Mixed)

  • 🎯 High/medium/low confidence categories

Related MCP server: AI Content Detector MCP Server

Installation

Claude Desktop

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on Mac or %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "gptzero": {
      "command": "npx",
      "args": ["-y", "@louis030195/gptzero-mcp"],
      "env": {
        "GPTZERO_API_KEY": "your-gptzero-api-key"
      }
    }
  }
}

Claude Code

# Install globally in user scope with API key
claude mcp add -s user gptzero npx -e GPTZERO_API_KEY=your-gptzero-api-key -- -y @louis030195/gptzero-mcp

Get Your GPTZero API Key

  1. Go to GPTZero

  2. Sign up or log in

  3. Navigate to API settings

  4. Copy your API key

Usage

Once configured, you can use natural language to detect AI content:

  • "Check if this text is AI-generated: [paste text]"

  • "Analyze this document for AI detection"

  • "What's the probability this was written by AI?"

  • "Is this human or AI written?"

Tools

gptzero_detect

Detect if text was generated by AI.

Parameters:

  • document (string, required): Text to analyze

  • multilingual (boolean, optional): Enable multilingual detection for French/Spanish (default: false)

Returns:

  • Predicted class (ai/human/mixed)

  • Confidence category (high/medium/low)

  • Probability scores for each class

  • Detailed sentence-level analysis

gptzero_model_versions

Get available GPTZero model versions.

Returns: List of available model versions

Development

# Clone the repo
git clone https://github.com/louis030195/gptzero-mcp.git
cd gptzero-mcp

# Install dependencies
npm install

# Build
npm run build

# Run locally
GPTZERO_API_KEY=your-api-key npm start

API Documentation

See GPTZero API Docs for full API details.

License

MIT

Author

Louis Beaumont

Available Tools

2 tools
gptzero_detectA

Detect if text was generated by AI. Returns probability scores for AI, human, and mixed content.

ParametersJSON Schema
NameRequiredDescriptionDefault
documentYesThe text document you want to analyze for AI detection
multilingualNoEnable multilingual detection (supports French and Spanish)

TDQS

A3.5/5.0
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 mentions the return values (probability scores) but lacks details on accuracy, limitations, rate limits, or authentication needs. For a detection tool with zero annotation coverage, this is a significant gap in transparency.

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, efficient sentence that front-loads the core purpose and output without any wasted words. It is appropriately sized for the tool's complexity, making it easy to understand quickly.

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?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and output but lacks behavioral context and usage guidelines. Without an output schema, it should ideally explain return values more, but the mention of probability scores provides some compensation.

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 both parameters thoroughly. The description does not add any meaning beyond what the schema provides, such as explaining the implications of the 'multilingual' parameter or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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's purpose with a specific verb ('Detect') and resource ('text generated by AI'), and it distinguishes from the sibling tool 'gptzero_model_versions' by focusing on detection rather than model version management. It specifies the output ('probability scores for AI, human, and mixed content'), making the purpose explicit and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for analyzing text for AI detection, but it does not provide explicit guidance on when to use this tool versus alternatives or any exclusions. No context is given about scenarios where it might be preferred over other methods, leaving usage to inference based on the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

gptzero_model_versionsB

Get available GPTZero model versions

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Get available GPTZero model versions', which implies a read-only operation, but doesn't specify details like authentication needs, rate limits, error handling, or what the return format might be. This leaves significant gaps for a tool with zero annotation coverage.

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, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.

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?

Given the tool's simplicity (0 parameters, no output schema), the description is minimally adequate but lacks depth. It doesn't explain what 'model versions' entail or provide behavioral context, which is a gap since there are no annotations to compensate. However, for a straightforward read operation, it's passable but not comprehensive.

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, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, but it implicitly confirms no inputs are required by not mentioning any. This meets the baseline for 0 parameters, though it could slightly enhance clarity by explicitly stating 'no parameters needed'.

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 'Get available GPTZero model versions' clearly states the verb ('Get') and resource ('available GPTZero model versions'), making the purpose understandable. However, it doesn't distinguish this tool from its sibling 'gptzero_detect', which appears to be a different detection function, so it misses full sibling differentiation.

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 versus alternatives, such as the sibling 'gptzero_detect'. It lacks any context about prerequisites, timing, or exclusions, leaving usage entirely implicit based on the tool name alone.

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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TDQS

B3.4/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one detects AI-generated text with probability scores, while the other lists available model versions. There is no overlap or ambiguity between these functions, making it easy for an agent to choose the right tool for each task.

Naming Consistency5/5

Both tools follow a consistent naming pattern with the prefix 'gptzero_' followed by a descriptive verb_noun combination (detect and model_versions). This uniformity enhances readability and predictability across the tool set.

Tool Count2/5

With only two tools, the server feels thin and under-scoped for a domain like AI text detection. While the tools cover core detection and model info, typical workflows might require additional operations such as batch processing, history tracking, or configuration management, suggesting a need for more comprehensive coverage.

Completeness3/5

The server provides basic functionality for AI detection and model version retrieval, but there are notable gaps. For example, it lacks tools for managing detection history, customizing detection parameters, or handling batch analyses, which could limit agent effectiveness in more complex scenarios within this domain.

Maintenance

ActivityInactive
ResponsivenessSyncing

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