GPTZero MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GPTZERO_API_KEY | Yes | Your GPTZero API key |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| gptzero_detectA | Detect if text was generated by AI. Returns probability scores for AI, human, and mixed content. |
| gptzero_model_versionsB | Get available GPTZero model versions |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
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.
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.
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.
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.