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io.github.AIops-tools/symantec-clouddlp-aiops

io.github.AIops-tools/symantec-clouddlp-aiops

by AIops-tools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

clouddlp_validators

Read-only

Lists data identifier validators so you can review available validation rules for CloudSOC DLP data identifiers.

Instructions

[READ] List data identifier validators.

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": "clouddlp_validators"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

The annotations already declare readOnlyHint=true, and the description's "[READ]" tag merely restates that without adding context. Nothing is said about pagination, result volume, or what a validator record contains, so behavior beyond the safety hint is undisclosed.

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

Conciseness4/5

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

A single front-loaded sentence with no filler. The "[READ]" prefix is redundant with the readOnlyHint annotation, a small waste in an otherwise tight definition.

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

Completeness3/5

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

For a zero-argument list tool the description is minimally sufficient, but it never explains what a validator is, how it relates to data identifiers, or what the list returns. No output schema exists, so some of that burden falls on the description and is unmet.

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?

The tool takes zero parameters, so there is no parameter meaning to convey; the schema is empty and fully described. Baseline 4 applies for a parameterless tool.

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

Purpose4/5

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

The description gives a specific verb plus resource ("List data identifier validators"), so the agent knows this is a read/list operation over a named collection. It does not differentiate from close siblings such as clouddlp_data_identifiers or clouddlp_data_identifier, leaving the boundary between "validators" and "data identifiers" unclear.

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?

There is no guidance on when to call this rather than clouddlp_data_identifiers or clouddlp_data_identifier. No prerequisites, no exclusions, and no stated context of use.

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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