ai-guardrail-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": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| aiguard_statusA | Show Input/output policy validation configuration / health. |
| aiguard_scanC | Scan text for policy/safety issues. |
| aiguard_suiteC | Generate a test suite outline. |
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 3 tools
The three tools have clearly distinct purposes: status/health checking, text scanning, and test suite generation. There is no overlap or ambiguity between them, making tool selection straightforward.
All tool names follow a uniform aiguard_ prefix pattern with a single lowercase descriptive word. This consistent naming convention makes the tool set predictable and easy to navigate.
Three tools is a compact but reasonable size for a focused guardrail utility. Each tool has a distinct purpose, though the set could potentially be slightly expanded with additional related operations without feeling bloated.
The tools cover health/status, text scanning, and test outline generation, but notable gaps exist—there is no policy management or execution capability for the generated test suite. Specifically, aiguard_suite creates an outline with no way to run it, creating a dead end in the workflow.