Skip to main content
Glama
cody-aigov
by cody-aigov

ai_red_team

Generate adversarial test cases for an AI system prompt. Produces a structured runbook with attack categories to identify vulnerabilities.

Instructions

Generate adversarial test cases for an AI system prompt (SEC-005).

Produces a red team runbook: specific adversarial inputs tailored to the system prompt, organized by attack category. Returns a structured framework for the host to generate the test cases.

Args: system_prompt: The system prompt to red team. num_test_cases: Number of test cases to generate (default 10, max 30).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
system_promptYes
num_test_casesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations are absent, so the description must cover behavior. It states the output is a 'red team runbook' and 'structured framework', but does not disclose any side effects, permissions, or rate limits. Adequate but minimal.

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 concise with two paragraphs. Front-loaded with the core purpose, followed by parameter details. Every sentence adds value, no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description need not detail return values. It covers purpose, output type, and parameters effectively. Could benefit from a brief example or more detail on the runbook structure, but is largely complete for a simple tool.

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?

Schema coverage is 0%, but the description includes an 'Args' section that explains both parameters: system_prompt (the prompt to test) and num_test_cases (default 10, max 30). Adds significant meaning beyond the schema's bare titles and types.

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?

Clearly specifies the verb 'Generate adversarial test cases' and the resource 'AI system prompt', with a security procedure reference (SEC-005). Distinguishes itself from siblings ai_risk_classify and ai_safety_screen by its specific red-teaming focus.

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 testing AI prompts, but lacks explicit guidance on when to use this tool versus siblings or when not to use it. No exclusions or alternatives mentioned.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/cody-aigov/ai-governance-controls'

If you have feedback or need assistance with the MCP directory API, please join our Discord server