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Atomic Red Team MCP

by cyberbuff
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Python
Remote

Atomic Red Team MCP Server

An MCP (Model Context Protocol) server that provides access to Atomic Red Team tests.

Available Tools and Resources

The server provides the following MCP tools:

  • query_atomics - Search atomics by technique ID, name, description, or platform

  • refresh_atomics - Download latest atomics from GitHub

  • validate_atomic - Validate atomic test YAML

  • get_validation_schema - Get the atomic test schema

  • execute_atomic - Execute atomic tests (requires ART_EXECUTION_ENABLED=true)

And resources:

  • file://documents/{technique_id} - Read atomic test files by technique ID

Usage Examples

  • "Search mshta atomics for windows"

  • "Show me all the atomic tests for T1059.002"

  • "Find all the applescript atomics for macOS"

  • "Validate this atomic test YAML "

Related MCP server: llamator-mcp-server

Installation

The Atomic Red Team MCP server can be installed in various development tools and AI assistants. Choose your platform below for detailed installation instructions:

Quick Start

Recommended: Using uvx

uvx atomic-red-team-mcp

Using Docker

docker run --rm -i ghcr.io/cyberbuff/atomic-red-team-mcp:latest

Platform-Specific Guides

Installation Methods

Each platform supports multiple installation methods:

  1. uvx (Recommended) - Easiest setup, automatic updates

  2. Docker - Isolated environment, consistent across systems

  3. Remote Server ⚠️ - Hosted on Railway (free tier, may have limits)

Configuration

Environment Variables

Check the .env.example file for a list of environment variables and their default values.

Server Configuration

  • ART_MCP_TRANSPORT - Transport protocol (stdio, sse, streamable-http)

  • ART_MCP_HOST - Server host address (default: 0.0.0.0)

  • ART_MCP_PORT - Server port number (default: 8000)

Repository Configuration

  • ART_GITHUB_URL - GitHub URL for atomics repository (default: https://github.com)

  • ART_GITHUB_USER - GitHub user/org (default: redcanaryco)

  • ART_GITHUB_REPO - Repository name (default: atomic-red-team)

  • ART_DATA_DIR - Local directory path where atomic test files are stored (default: ./atomics)

Security Configuration

  • ART_EXECUTION_ENABLED - Enable the execute_atomic tool (default: false). Set to true, 1, or yes to enable. ⚠️ WARNING: Only enable in controlled environments as this allows executing potentially dangerous security tests.

  • Enable Authentication if you are hosting a remote MCP server

Authentication Configuration

  • ART_AUTH_TOKEN - Static bearer token for authentication (optional, authentication disabled if not set)

  • ART_AUTH_CLIENT_ID - Client identifier for authenticated requests (default: authorized-client)

Enabling Atomic Test Execution

By default, the execute_atomic tool is disabled for safety reasons. To enable it:

# Using uvx
ART_EXECUTION_ENABLED=true uvx atomic-red-team-mcp

⚠️ Security Warning: Only enable atomic test execution in controlled, isolated environments (like test VMs or sandboxes). These tests can modify system state, create files, execute commands, and perform actions that may be flagged as malicious by security tools.

Authentication

The server supports static token authentication for securing access to the MCP tools and resources. When enabled, clients must include a bearer token in the Authorization header:

Authorization: Bearer <your-token>

To enable authentication:

  1. Set the ART_AUTH_TOKEN environment variable:

    export ART_AUTH_TOKEN="your-secure-token-here"

  2. Start the server (authentication is automatically enabled)

  3. Clients authenticate by including the token in requests:

    curl -H "Authorization: Bearer your-secure-token-here" http://localhost:8000

Security Notes:

  • Authentication is disabled by default (no token required)

  • When ART_AUTH_TOKEN is set, all requests must include a valid bearer token

  • Use strong, randomly generated tokens in production

  • Never commit tokens to version control

  • For development/testing, use a simple token. For production, use a cryptographically secure token

Example with Docker:

docker run --rm -i \
  -e ART_AUTH_TOKEN="my-secure-token" \
  -e ART_AUTH_CLIENT_ID="my-client" \
  ghcr.io/cyberbuff/atomic-red-team-mcp:latest

Built With

Available Tools

6 tools
generate_atomicA
Read-only

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

ParametersJSON Schema
NameRequiredDescriptionDefault
platformNolinux
descriptionNo
technique_idYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
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)

TDQS

A4.6/5.0
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.

get_validation_schemaA
Read-onlyIdempotent

Get the JSON schema that defines the structure and requirements for atomic tests.

This schema provides the complete specification for creating valid atomic tests. It defines all fields (required and optional), data types, validation rules, and constraints. Use this as a reference when creating or modifying atomic tests to ensure they meet quality standards.

The schema follows the Atomic Red Team YAML format and is automatically generated from the Pydantic models, ensuring it's always in sync with validation rules.

Returns: dict: JSON Schema (Draft 7) containing: - definitions: Nested object definitions (Executor, Dependency, etc.) - properties: Field definitions with types and constraints - required: List of mandatory fields - additionalProperties: Whether extra fields are allowed - field descriptions: Human-readable explanations for each field

Schema Structure: The schema defines these main sections: - name: Test name (required, min 1 character) - description: Test explanation (required, min 1 character) - supported_platforms: Platform list (required, min 1 platform) - executor: Execution method (required, CommandExecutor or ManualExecutor) - input_arguments: Parameterized inputs (optional, dict) - dependencies: Prerequisites (optional, list) - dependency_executor_name: Executor for dependencies (optional) - auto_generated_guid: Unique ID (optional, auto-generated)

Examples: # Get the schema schema = get_validation_schema()

# Check required fields
required_fields = schema['required']
print(f"Required fields: {required_fields}")

# View field definitions
properties = schema['properties']
print(f"Available fields: {list(properties.keys())}")

# Check platform options
platform_enum = schema['definitions']['Platform']['enum']
print(f"Valid platforms: {platform_enum}")

Common Use Cases: 1. Creating new tests: Reference required fields and formats 2. Understanding validation: See what rules will be enforced 3. Tool development: Use schema for code generation 4. Documentation: Generate field descriptions automatically

Notes: - Schema is generated from Pydantic models at runtime - Always reflects current validation rules - Includes custom validators and constraints - Follows JSON Schema Draft 7 specification - Can be used with JSON Schema validators in any language - Do not add comments to the created atomic test

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the description adds extra value by disclosing that the schema is generated at runtime from Pydantic models, is always in sync, and includes a note against adding comments. This goes beyond the basic safety profile.

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?

The description is long but well-structured into sections (schema structure, examples, use cases, notes). The first sentence delivers the core purpose, and each section adds practical information. Slightly verbose for a no-parameter tool, but the content justifies the length.

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?

The tool is simple, but the description goes beyond the minimum by detailing the return value structure, providing code examples, and explaining how the schema is generated. With a full output schema and a comprehensive description, nothing important is left unstated.

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 has zero parameters, so schema coverage is trivially 100%. The description does not need to explain parameters and instead focuses on return value structure, which is appropriate for a no-argument tool.

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+resource: 'Get the JSON schema that defines the structure and requirements for atomic tests.' This clearly distinguishes the tool from siblings like validate_atomic or generate_atomic by its unique purpose.

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 as a reference when creating or modifying atomic tests to ensure they meet quality standards' and lists common use cases. While it doesn't explicitly name alternatives, the context makes it clear this is the schema-reference tool, not a validation or generation tool.

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

query_atomicsA
Read-onlyIdempotent

Search and filter atomic tests across the repository.

This tool searches through all atomic tests and returns matches based on your criteria. You can search by free-text query, or filter by specific attributes like technique ID, GUID, or platform. Results are paginated — use the returned next_cursor to fetch subsequent pages.

Args: query: Free-text search term to match against all atomic test fields including name, description, commands, and input arguments. Supports multi-word queries where all words must match (AND logic). Examples: "powershell registry", "credential access", "T1059"

guid: Filter by exact atomic test GUID (UUID format).
      Example: "a8c41029-8d2a-4661-ab83-e5104c1cb667"
      Use this when you know the specific test you want to retrieve.

technique_id: Filter by MITRE ATT&CK technique ID. Must follow the format
              T#### or T####.### (e.g., T1059, T1059.001).
              Example: "T1059.001" for PowerShell technique
              Returns all atomic tests associated with this technique.

technique_name: Filter by technique name (case-insensitive partial match).
                Example: "Command and Scripting Interpreter"
                Useful when you know the technique name but not the ID.

supported_platforms: Filter by platform (case-insensitive partial match).
                    Valid platforms: windows, linux, macos, office-365, azure-ad,
                    google-workspace, saas, iaas, containers, iaas:aws, iaas:azure,
                    iaas:gcp, esxi
                    Example: "windows", "linux", "macos"

cursor: Opaque pagination cursor returned by a previous call as `next_cursor`.
        Omit or pass null to start from the first page.

limit: Maximum number of results to return per page (1–200, default 50).

Returns: QueryAtomicsOutput: Structured output containing: - total_results: Total number of matching atomic tests - atomics: List of matching atomic tests for this page - next_cursor: Opaque cursor for the next page, or null if last page - query_metadata: Information about applied filters

Raises: ValueError: If query is empty without any filters ValueError: If query exceeds 1000 characters ValueError: If technique_id format is invalid (must be T#### or T####.###) ValueError: If limit is outside the range 1–200 ValueError: If cursor is malformed

ParametersJSON Schema
NameRequiredDescriptionDefault
guidNo
limitNo
queryYes
cursorNo
technique_idNo
technique_nameNo
supported_platformsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
atomicsYesList of matching atomic tests
next_cursorNoOpaque cursor to pass as `cursor` on the next call. Null when this is the last page.
total_resultsYesTotal number of atomic tests matching the query
query_metadataNoMetadata about the query execution (filters applied, etc.)

TDQS

A4.7/5.0
Behavior5/5

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

The annotations declare readOnlyHint=true and idempotentHint=true, but the description goes far beyond these by detailing pagination via `next_cursor`, multi-word AND logic, exact format requirements for technique_id and GUID, valid platform values, and specific ValueError conditions. This gives the agent a comprehensive model of the tool's runtime behavior beyond the safety hints.

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?

The description is well-structured with Args, Returns, and Raises sections, making it easy to scan. However, there is slight redundancy between the first sentence 'Search and filter atomic tests' and the second sentence 'This tool searches through all atomic tests,' which prevents a perfect score. Still, every other sentence adds necessary detail.

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?

With 7 parameters, no schema descriptions, and an output schema, the description leaves nothing essential uncovered. It specifies parameter constraints, pagination behavior, error conditions, and return structure. It fully compensates for the 0% schema description coverage and is complete enough for correct invocation.

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?

Schema description coverage is 0%, so the description carries the full burden of documenting parameters. It explains each parameter's purpose, format, examples, and defaults (e.g., limit 1–200, cursor null for first page, valid platform list). This is high-value semantic enrichment that the bare schema entirely lacks.

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 'Search and filter atomic tests across the repository,' which is a specific verb+resource statement. It clearly differentiates from siblings like validate_atomic or generate_atomic by focusing on querying/filtering. Even without explicit sibling comparisons, the purpose is unambiguous.

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 provides clear context for when to use this tool: whenever searching or filtering atomic tests with free-text or structured filters. It does not explicitly mention alternatives or exclusions, but the detailed parameter documentation (e.g., filtering by GUID, technique ID, platform) implies the tool's role. A 5 would require explicit 'use X instead' guidance, which is absent.

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

refresh_atomicsA
Idempotent

Download and reload atomic tests from the GitHub repository.

This tool forces a fresh download of all atomic tests from the configured GitHub repository, replacing any existing local copies. It then reloads all tests into memory, making them immediately available for querying and execution.

Use this tool when:

  • You want to get the latest atomic tests from the repository

  • Custom atomic tests were added to the data directory

  • The atomic test database needs to be refreshed

  • You suspect the loaded tests are out of sync with the repository

Args: ctx: MCP context (provided automatically by the framework) progress: Background task progress reporter (injected automatically)

Returns: RefreshAtomicsOutput: Structured output containing: - success (bool): Whether the refresh operation completed successfully - message (str): Human-readable message about the refresh operation - atomics_count (int): Number of atomic tests loaded after refresh - repository_url (str): GitHub repository URL that was used for refresh

Process: 1. Deletes existing atomic tests directory (if present) 2. Clones the GitHub repository (configured via ART_GITHUB_* settings) 3. Extracts the atomics directory from the repository 4. Parses all YAML files and validates them 5. Loads atomic tests into server memory 6. Makes tests immediately available to other tools

Configuration: The repository location is controlled by environment variables: - ART_GITHUB_URL: Base GitHub URL (default: https://github.com) - ART_GITHUB_USER: User/organization (default: redcanaryco) - ART_GITHUB_REPO: Repository name (default: atomic-red-team) - ART_DATA_DIR: Local storage path (default: ./atomics)

Examples: # Refresh from default repository refresh_atomics(ctx)

# After setting custom repo in .env:
# ART_GITHUB_USER=your-org
# ART_GITHUB_REPO=custom-atomics
refresh_atomics(ctx)

Notes: - This operation may take 30-60 seconds depending on network speed - Runs as a background task — the client receives a task ID immediately and can poll for completion - Requires internet connectivity to GitHub - Overwrites any local modifications to atomic tests - The repository is cloned with depth=1 for efficiency (only latest commit) - Failed YAML files are logged but don't stop the overall refresh

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
messageYesHuman-readable message about the refresh operation
successYesWhether the refresh operation completed successfully
atomics_countYesNumber of atomic tests loaded after refresh
repository_urlYesGitHub repository URL that was used for refresh

TDQS

A4.2/5.0
Behavior1/5

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

The description explicitly states the tool will 'Deletes existing atomic tests directory', 'Overwrites any local modifications', and 'replaces existing local copies', all of which indicate destructive actions. However, the annotation sets destructiveHint=false, which directly contradicts this behavioral disclosure. Per the scoring rules, this contradiction forces a score of 1 on this dimension.

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?

Despite being long, the description is excellently structured with labeled sections (Overview, Use when, Args, Returns, Process, Configuration, Examples, Notes). It front-loads the core purpose, and every section conveys actionable details without redundancy or filler.

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?

The description covers the six-step process, environment configuration, example invocations, runtime expectations, background execution, error tolerance for YAML failures, and the exact output fields. This level of detail, combined with the output schema, fully contextualizes the tool for an agent with no prior knowledge.

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 has zero properties, and the description explains that ctx and progress are automatically injected by the framework. This clarifies the empty schema, confirming no user-supplied parameters are needed and adds meaningful context about the automatic injection mechanism.

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 phrase 'Download and reload atomic tests from the GitHub repository', clearly naming both the action and the resource. It distinguishes itself from sibling tools like query_atomics, validate_atomic, and generate_atomic by focusing solely on refreshing the local test data.

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

Usage Guidelines5/5

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

The 'Use this tool when' section provides four concrete, scenario-based conditions for when to invoke this tool. It also implicitly warns against expecting a fast operation and notes prerequisites like internet connectivity and the overwrite of local modifications, effectively guiding the agent on when this tool is appropriate.

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

server_infoA
Read-onlyIdempotent

Get comprehensive information about the MCP server configuration and environment.

This tool returns server metadata including version, transport protocol, operating system, and data directory location. Use this to:

  • Verify server configuration

  • Check server version for compatibility

  • Confirm the platform before executing atomic tests

  • Locate the atomic tests data directory

Args: ctx: MCP context (provided automatically by the framework)

Returns: ServerInfoOutput: Server information with the following fields: - name (str): Server name - always "Atomic Red Team MCP" - version (str): Installed package version (e.g., "1.2.3") Shows "dev" if running from source without installation - transport (str): MCP transport protocol being used Values: "stdio" (default), "sse", or "streamable-http" - os (str): Operating system platform Values: "Darwin" (macOS), "Linux", "Windows" Use this to verify test compatibility before execution - data_directory (str): Absolute path to atomic tests storage directory This is where atomic YAML files are stored Use this path when creating new atomic tests - execution_enabled (bool): Whether atomic test execution is enabled on this server

Examples: # Get server information info = server_info(ctx) print(f"Running version {info.version} on {info.os}")

# Check if remote server for execution
if info.transport == 'streamable-http':
    print("This is a remote MCP server")

# Get data directory for creating tests
data_dir = info.data_directory
print(f"Create new tests in: {data_dir}/T####/T####.yaml")

Use Cases: 1. Before executing tests: Check OS matches supported_platforms 2. Creating atomic tests: Use data_directory to know where to save files 3. Debugging: Verify configuration settings 4. Version compatibility: Ensure tools match server version

Notes: - This tool always succeeds and never raises exceptions - Information reflects the current runtime configuration - Transport and data_directory come from Settings (environment variables/.env) - OS is detected at runtime and cannot be changed

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
osYesOperating system platform (Darwin, Linux, Windows)
nameYesServer name
versionYesServer version number
transportYesMCP transport protocol being used (stdio, sse, streamable-http)
data_directoryYesAbsolute path to atomic tests storage directory
execution_enabledYesWhether atomic test execution is enabled on this server

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses that the tool always succeeds and never raises exceptions, reflects current runtime configuration, derives transport and data_directory from settings, and detects OS at runtime. It also details each return field's possible values, adding significant context.

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?

The description is lengthy but well-structured with sections, bullet lists, examples, and notes. It is front-loaded with the main purpose, followed by return field details, examples, and use cases. Although some redundancy exists between the 'Use this to' list and the 'Use Cases' section, every section earns its place and the organization makes it scannable.

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 tool with no parameters and rich annotations, the description covers all necessary context: return field semantics, example usage, use cases, environment variable dependencies, and error behavior. It even provides a path template for creating tests, making it fully self-contained.

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?

There are no user-supplied parameters; the schema coverage is 100%. The description mentions the framework-provided ctx only as a placeholder. With zero parameters, the baseline is 4, and no further parameter explanation is needed.

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 clear verb and resource: 'Get comprehensive information about the MCP server configuration and environment.' It distinguishes this tool from siblings like query_atomics and generate_atomic by focusing on server metadata. The use cases further clarify its unique role.

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

Usage Guidelines5/5

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

The description provides an explicit 'Use this to' list covering verification, compatibility checks, platform confirmation before atomic tests, and locating the data directory. The 'Use Cases' section adds concrete scenarios such as before executing tests and creating atomic tests. Although no alternative tools are named, the guidance is complete for this unique informational tool.

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

validate_atomicA
Read-onlyIdempotent

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

ParametersJSON Schema
NameRequiredDescriptionDefault
yaml_stringYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
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)

TDQS

A4.8/5.0
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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 6 tool updatesv1.3.2
    • First observedgenerate_atomic
    • First observedget_validation_schema
    • First observedquery_atomics
    • First observedrefresh_atomics
    • First observedserver_info
    • First observedvalidate_atomic
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TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: server_info for configuration, refresh_atomics for downloading updates, query_atomics for searching, get_validation_schema for schema reference, validate_atomic for checking validity, and generate_atomic for AI-assisted creation. There is no ambiguity or overlap among the tools.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (e.g., refresh_atomics, query_atomics, validate_atomic). The naming is predictable and uniform, making it easy to infer tool behavior from the name.

Tool Count5/5

Six tools is well-scoped for an Atomic Red Team server. Each tool covers a distinct aspect of the atomic test lifecycle (configuration, refresh, query, schema, validation, generation) without redundancy or bloat.

Completeness4/5

The tool set covers the core workflows: querying, validating, generating, and updating atomic tests. Minor gaps include lack of direct create/update/delete tools for saving tests to disk and no execution tool, but server_info provides the data directory path and the generation flow supports creating tests.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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