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KevinRabun

io.github.KevinRabun/GDPRShiftLeftMCP

by KevinRabun

analyze_code_for_role_indicators

Analyzes source code patterns to determine GDPR controller vs processor role based on data collection, consent, and data forwarding indicators.

Instructions

Analyze source code for patterns indicating controller vs processor role.

Detects patterns like: direct user data collection, consent mechanisms, multi-tenant isolation, webhook receivers, data forwarding, etc.

Args: code: The source code to analyze language: Programming language ('python', 'typescript', 'csharp', etc.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
languageYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description explains the tool detects patterns in code but does not disclose any behavioral traits such as performance, limitations, or absence of side effects. With no annotations, the description carries the burden; it provides minimal behavioral insight beyond the operation.

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 two paragraphs plus an argument list, compact and to the point. Every sentence adds value, and the format is front-loaded with the core purpose followed by examples and argument details.

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

Completeness4/5

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

Given the output schema exists, the description does not need to explain return values. It provides a clear purpose and lists patterns detected, which gives sufficient context. However, it does not mention what form the analysis results take (e.g., classification or list), but this is minor given the schema.

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?

Schema description coverage is 0%, so the description must add meaning. It explains that 'code' is the source code to analyze and 'language' is the programming language with examples. This adds value beyond the schema, though it does not enumerate all possible languages or specify format constraints.

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 it analyzes source code for controller vs processor role patterns, listing specific indicators like direct user data collection and consent mechanisms. This makes the tool's purpose distinct from sibling tools like assess_controller_processor_role or analyze_application_code.

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

Usage Guidelines3/5

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

The description implies usage when source code is available but provides no explicit guidance on when to use this tool versus alternatives like assess_controller_processor_role or when not to use it. No prerequisites or exclusions are mentioned.

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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