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RedTeam ML API MCP

RedTeam ML API MCP

by rnadjqaim
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

evaluate_text

Assess a single ML API response for defensive risk signals to identify vulnerabilities in LLM endpoints.

Instructions

Evaluate one ML API response for defensive risk signals.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "evaluate_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the tool evaluates for risk signals but does not explain what that entails—no mention of safety (read-only?), side effects, authorization needs, or whether it modifies state. The description is too vague to inform the agent about consequences.

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

Conciseness3/5

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

The description is a single sentence, which is concise but lacks structure. It front-loads the verb and resource but misses the opportunity to include key details like output or usage. Every word is functional, but the sentence could be restructured to include more information without losing conciseness.

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

Completeness2/5

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

Given the tool's simplicity (one string parameter, no output schema, no annotations), the description is incomplete. It does not explain the return value (e.g., risk score, flagged issues) or any behavioral details. An agent would lack sufficient context to use this tool effectively, especially when sibling tools have richer descriptions.

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

Parameters3/5

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

Although the input schema provides no description for the 'text' parameter (0% coverage), the tool description adds meaning by implying the text should be an ML API response for risk evaluation. This clarifies the parameter's purpose but does not specify format, allowed values, or constraints, so it provides moderate added value.

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?

The description clearly states the tool evaluates a single ML API response for defensive risk signals. It uses a specific verb ('evaluate') and resource ('ML API response') and differentiates from siblings by focusing on defensive risk signals rather than generation, reporting, or pentesting.

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?

The description provides no guidance on when to use this tool versus alternatives like run_ai_api_pentest or list_attack_packs. No context is given for prerequisites, limitations, or typical use cases, leaving the agent without decision criteria.

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

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