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RedTeam ML API MCP

RedTeam ML API MCP

by rnadjqaim
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

generate_operating_model

Create a mindset-shift operating model for security, red team, AI architecture, platform, and executives to align adversarial testing with compliance frameworks.

Instructions

Generate a mindset-shift operating model for security, red team, AI architecture, platform, and executives.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "generate_operating_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
run_resultNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

D1.8/5.0
Behavior1/5

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

No annotations are provided, and the description does not disclose any behavioral traits such as side effects, authentication needs, input/output specifics, or internal processing. The tool's name implies generation, but no operational details are given.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise but overly terse. It lacks structure and fails to provide essential details, making it insufficient for effective use.

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

Completeness1/5

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

Given the tool's complexity (generating an operating model) and the lack of output schema or parameter details, the description is critically incomplete. The agent cannot infer what the tool returns or how to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter 'run_result' with no description and 0% schema coverage. The description does not explain its purpose, format, or relationship to the output, leaving the agent with no guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool generates an operating model for security, red team, AI architecture, platform, and executives, but 'mindset-shift operating model' is vague and the verb 'generate' is generic. It does not clearly differentiate from sibling tools like 'generate_report'.

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?

No guidance on when to use this tool versus alternatives (e.g., generate_report, run_red_team). No exclusions or prerequisites are mentioned.

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

Deploy Server

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