pangram-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyzeA | Detect AI-generated text with Pangram Labs. Classifies the input as human-written, AI-generated, or AI-assisted and returns an
overall verdict ( Requires the PANGRAM_API_KEY environment variable. |
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 1 tool
With only a single tool, there is no possibility of overlap or misselection. The tool's purpose (AI-text detection) is unambiguous.
A lone tool named 'analyze' follows a clear verb-style convention and is readable. With one tool there is no convention to violate.
The server wraps a single-purpose detection API, so one tool is defensible, but it feels thin for anything beyond a one-shot call. There is no batch, async, or configuration surface.
The core detection operation is fully covered, returning verdicts, class fractions, and per-segment windows. Minor gaps exist for batch/multi-document analysis or history retrieval, but agents can work around them.