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KevinRabun

io.github.KevinRabun/GDPRShiftLeftMCP

by KevinRabun

generate_dsr_workflow

Generate a step-by-step workflow for fulfilling Data Subject Requests (DSR) with Azure implementation notes. Specify request type and optional system context.

Instructions

Generate a step-by-step DSR fulfilment workflow with Azure implementation notes.

Args: request_type: Type of DSR system_context: Optional description of the system architecture

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
request_typeYes
system_contextNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description bears full responsibility. It only states the generation action without disclosing side effects, idempotency, authorization needs, or any behavioral traits. The agent is left guessing about the tool's behavior.

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 extremely concise: two sentences plus a parameter list. It is front-loaded with the core purpose and includes no extraneous information. Every element earns its place.

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

Completeness3/5

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

Given the output schema exists, return values are covered. The two parameters are documented. However, the tool lacks usage guidelines and behavioral transparency, making it feel incomplete for a workflow generator that likely has a complex process.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It adds a brief explanation for each parameter: request_type as 'Type of DSR' and system_context as 'Optional description of the system architecture'. While minimal, this provides basic semantics beyond the schema's titles.

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 clearly states the tool generates a step-by-step DSR fulfilment workflow with Azure implementation notes. This verb+resource combination is specific and distinct from sibling tools like get_dsr_guidance or get_dsr_timeline.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention context, prerequisites, or exclude scenarios, leaving the agent without decision support.

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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