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cyberbuff

Atomic Red Team MCP

by cyberbuff

validate_atomic

Read-onlyIdempotent

Validate atomic test YAML against the official Atomic Red Team schema. Get structural checks, required field verification, and best practice warnings.

Instructions

Validate an atomic test YAML string against the official Atomic Red Team schema.

This tool checks if your atomic test follows the correct structure and includes all required fields. Use this before finalizing any atomic test to ensure it meets the quality standards and can be properly parsed by Atomic Red Team tools.

The validator performs two levels of checks:

  1. Structural validation: Ensures all required fields are present and properly typed

  2. Best practice warnings: Flags common issues that should be addressed

Args: yaml_string: The complete YAML string of the atomic test to validate. Should include all fields like name, description, supported_platforms, executor, etc. as defined in the schema.

Returns: ValidationOutput: Structured validation result containing: - valid (bool): Whether the atomic test passes validation - message (str): Human-readable success/error message with warnings prominently displayed - atomic_name (str): Name of the atomic test (only if valid) - supported_platforms (list): Platforms the test supports (only if valid) - warnings (list): List of warning messages for best practice violations (only if present) - error (str): Detailed error message (only if invalid)

Validation Warnings: The tool will flag these common issues with ⚠️ warnings: - Presence of 'auto_generated_guid' field (should be auto-generated, not manually set) - Use of echo/print/Write-Host commands (discouraged in test commands)

Warnings do not cause validation to fail, but should be addressed before finalizing.

Examples: # Valid atomic test yaml_str = ''' name: Test PowerShell Execution description: Execute a PowerShell command supported_platforms: - windows executor: name: powershell command: Get-Process ''' result = validate_atomic(yaml_str, ctx) # result.valid == True, result.message contains success message

# Test with warnings (still valid but needs improvement)
yaml_str = '''
name: Test with Echo
description: Test with echo command
supported_platforms:
  - linux
executor:
  name: bash
  command: echo "Hello World"
'''
result = validate_atomic(yaml_str, ctx)
# result.valid == True, result.warnings contains warning messages

# Invalid atomic test (missing required field)
yaml_str = '''
name: Incomplete Test
description: Missing supported_platforms
executor:
  name: bash
  command: ls
'''
result = validate_atomic(yaml_str, ctx)
# result.valid == False, result.error contains error message

Raises: No exceptions are raised - all errors are returned in the ValidationOutput model.

Notes: - Always check the 'valid' field before using the atomic test - Address all warnings even if validation succeeds - Warnings are displayed with ⚠️ emoji for visibility - The 'message' field contains formatted text with warnings prominently shown

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yaml_stringYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoDetailed error message (only if invalid)
validYesWhether the atomic test passed structural validation
messageYesHuman-readable validation message with warnings prominently displayed
warningsNoList of best practice warnings that should be addressed
atomic_nameNoName of the atomic test (only if valid)
supported_platformsNoPlatforms the test supports (only if valid)
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint), the description discloses the two-level validation process, specific warning types (auto_generated_guid, echo/print), that no exceptions are raised, and that warnings are formatted with ⚠️. It also advises checking the 'valid' field, providing rich behavioral context not available from structured fields.

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?

Although lengthy, the description is well-structured with Markdown headers, code blocks, and bullet points. Each section (Args, Returns, Warnings, Examples, Raises, Notes) adds value and is front-loaded with the core purpose. The length is justified by the tool's rich behavioral and return details.

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

Completeness5/5

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

Given the tool's simplicity (one parameter) and the presence of an output schema, the description goes beyond expectations by explaining return fields, warning types, error behavior, and usage notes. It is fully contextualized for an AI agent to invoke and interpret results correctly without ambiguity.

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

Parameters5/5

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

The input schema only provides a bare string type for yaml_string. The description compensates by defining the parameter as 'The complete YAML string of the atomic test to validate' and elaborates on required fields with examples, making the parameter semantics fully clear.

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 opens with a specific verb and resource: 'Validate an atomic test YAML string against the official Atomic Red Team schema.' This clearly defines the tool's function and distinguishes it from siblings like generate_atomic or query_atomics, which have different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states 'Use this before finalizing any atomic test' and explains the two levels of checks, which gives clear context for when to use the tool. It does not mention when not to use it or provide alternative tool names, so it stops short of a perfect score.

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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