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cyberbuff

Atomic Red Team MCP

by cyberbuff

generate_atomic

Read-only

Generate a validated atomic test for a MITRE ATT&CK technique using AI assistance, with automatic error fixing.

Instructions

Generate an atomic test for a MITRE ATT&CK technique using AI assistance.

Uses the MCP client's LLM to draft an atomic test YAML for the given technique and platform, then validates it automatically. If the generated test has errors or warnings, re-samples up to 3 times to fix them before returning.

Args: technique_id: MITRE ATT&CK technique ID (e.g., "T1059.001"). Used to focus the generated test on the correct technique.

platform: Target platform for the test. Valid values: windows, linux, macos.
          Defaults to "linux".

description: Optional free-text description of what the test should do or
             demonstrate. Leave blank to let the AI determine the best approach
             for the technique.

Returns: GenerateAtomicOutput: Result containing: - valid (bool): Whether the final test passes schema validation - message (str): Success or error message - atomic_name (str): Name of the generated test (if valid) - supported_platforms (list): Platforms declared in the test (if valid) - yaml (str): Generated YAML content (if valid) - warnings (list): Best-practice warnings to address (if any) - error (str): Validation error details (if invalid)

Notes: - Requires the MCP client to support server-side sampling - If the client doesn't support sampling, returns an error - The generated YAML is validated but NOT saved automatically - Use server_info to find where to save validated tests - Always review generated tests before use in production environments

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformNolinux
descriptionNo
technique_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
yamlNoGenerated YAML content (only if valid)
errorNoDetailed error message (only if invalid)
validYesWhether the generated test passed structural validation
messageYesHuman-readable validation message
warningsNoList of best practice warnings that should be addressed
atomic_nameNoName of the generated 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?

The description discloses meaningful behavior beyond annotations: it re-samples up to 3 times to fix validation errors, does not save the generated YAML automatically, requires server-side sampling support, and advises review before production. These details go far beyond the readOnlyHint/idempotentHint annotations.

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 well-organized and front-loaded with the purpose, but the detailed Returns section duplicates information likely available in the output schema. This adds unnecessary length, though the rest of the content is concise and valuable.

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?

For a generative tool with an output schema, the description is complete: it covers prerequisites, retry behavior, non-persistence, output fields, and follow-up actions. The combination of description, annotations, and output schema gives an agent everything needed to invoke the tool correctly.

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?

Despite 0% schema description coverage, the description explains every parameter: technique_id with an example, platform with valid values and default, and description with guidance on leaving it blank. This fully compensates for the schema's lack of descriptions.

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 starts with a specific verb and resource: 'Generate an atomic test for a MITRE ATT&CK technique using AI assistance.' This clearly distinguishes the tool from sibling validation and query tools, and the method (AI-assisted generation) is explicit.

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 gives a clear context: it is used to generate a new atomic test via AI, requires MCP server-side sampling, and points to server_info for saving. However, it does not explicitly contrast with siblings like validate_atomic or query_atomics, so it falls short of full when-not-to-use guidance.

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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