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ComplianceCow

ComplianceCow MCP Server

verify_control_in_assessment

Verify control existence in assessments and validate leaf control status to confirm rule attachment eligibility while checking current rule associations.

Instructions

Verify the existence of a specific control by alias within an assessment and confirm it is a leaf control.

CONTROL VERIFICATION AND VALIDATION:

  • Confirms the control with the specified alias exists in the given assessment.

  • Validates that the control is a leaf control (eligible for rule attachment).

  • Checks if a rule is already attached to the control.

  • Returns control details and attachment status.

LEAF CONTROL IDENTIFICATION:

  • A control is considered a leaf control if:

  • leafControl = true, OR

  • has no planControls array, OR

  • planControls array is empty.

  • Only leaf controls can have rules attached.

  • If the control is not a leaf control, an error will be returned.

Args: assessment_name: Name of the assessment. control_alias: Alias of the control to verify.

Returns: Dict containing control details, leaf status, and rule attachment info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assessment_nameYes
control_aliasYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Comprehensive disclosure of leaf control logic (three specific conditions), error behavior for non-leaf controls, and attachment status checking, compensating for absent annotations.

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?

Well-organized with clear headers and front-loaded purpose, though the leaf control definition section is slightly verbose for a tool description.

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?

Thoroughly covers domain-specific logic (leaf control eligibility), error conditions, and return structure, fully addressing the tool's complexity despite output schema existence.

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?

Provides essential semantic meaning for both parameters (assessment name and control alias) that the schema lacks, though definitions are basic.

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?

Explicitly states it verifies control existence by alias and confirms leaf status, clearly distinguishing it from sibling tools like attach_rule_to_control.

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?

Implies usage context (before rule attachment) by stating only leaf controls can have rules attached, but lacks explicit when-not guidance or comparison to fetch_controls.

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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