Atomic Red Team MCP

Server Quality Checklist

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Latest release: v1.3.2
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.
Average 4.6/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 8, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

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.
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.
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.
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.
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.
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.

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