GDPR Compliance for AI Systems MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool addresses a distinct GDPR compliance area (breach notification, processing classification, lawful basis, DPIA, rights requests, and EU AI Act mapping) with no overlap in purpose or functionality.
Naming Consistency5/5
All tool names follow a consistent descriptive_pattern (noun_verb or adjective_noun) using snake_case, e.g., breach_notification, classify_processing, dpia_generator.
Tool Count5/5
With 6 tools, the server covers the essential GDPR compliance tasks for AI systems without being too sparse or overly numerous. The count feels well-scoped for the domain.
Completeness4/5
The tool set covers major AI-relevant GDPR areas (DPIA, lawful basis, rights, breach, and crosswalk to AI Act). Minor gaps exist, such as tools for consent management or records of processing activities, but these are less critical for the stated purpose.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 8 community issues answered or closed in the last 6 months
- 35 commits in the last 12 weeks
- Last stable release on June 15, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as side effects, required permissions, performance implications, or rate limits. For a tool with no annotations, the description carries the full burden, but it only covers purpose and parameters.
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 a two-paragraph format: purpose first, then parameter documentation. It is front-loaded and efficient, though the Args block could be slightly more concise.
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?
The tool has an output schema (details not provided), and the description explains the main purpose and key parameters. However, it lacks behavioral details and misses the 'api_key' parameter in its description. For a regulatory mapping tool, more guidance on expected outputs or edge cases would improve completeness.
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 coverage is 0%, so the description must compensate. It includes an Args block explaining 'gdpr_articles' and 'focus_area' with some detail, but 'caller' and 'tier' are minimally described, and 'api_key' is missing from the Args block despite being in the schema. Thus, it adds value but is incomplete.
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 what the tool does: map GDPR requirements to EU AI Act obligations, specifying it shows where compliance satisfies, complements, or creates tension. This verb+resource+distinction clearly differentiates it from sibling tools like breach_notification or dpia_generator.
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 it is 'essential for organizations deploying AI in the EU that must comply with both regulations simultaneously,' which indicates when to use. However, it does not provide explicit when-not-to-use or compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose side effects, auth requirements, rate limits (though caller/tier hints), or what happens to data. Only mentions providing guidance, but behavioral traits beyond that are missing.
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?
Description is concise, front-loaded with purpose, and well-structured with an Args list. Every sentence adds value without redundancy.
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?
Handles 8 parameters (3 required) with output schema present. Description covers purpose and parameters but lacks usage guidance and behavioral transparency, leaving gaps for an agent to fully understand invocation context.
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%, but the description includes an Args section that explains each parameter's purpose (e.g., 'right_invoked: Which right is being exercised'), adding significant meaning beyond the schema which only has titles and types.
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?
Description explicitly states the tool handles data subject rights requests under GDPR Articles 15-22, listing specific rights (access, rectification, erasure, etc.), which clearly distinguishes it from sibling tools like breach_notification or dpia_generator.
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 for GDPR DSR handling but does not explicitly state when to use vs not use, nor does it mention alternatives or prerequisites. The step-by-step guidance mention is vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description bears full burden. It mentions rate limiting and tier via parameters but does not disclose side effects, idempotency, or data retention. Adequate but not exhaustive.
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 moderately sized with a clear opener and parameter list. Some redundancy exists (first sentence repeats determinations listed in Args), but overall efficient.
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 9 parameters (4 required) and an output schema present, the description covers parameter roles well. It does not explain error handling or return structure but is sufficient for agent selection.
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 compensates with brief explanations and examples for each parameter (e.g., 'Types of personal data processed (e.g. ["name", "email", "biometric", "health"])'). Adds value beyond schema names but lacks constraints or format specifics.
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 classifies data processing activities per GDPR articles, listing specific determinations (applicable articles, DPIA, special category, automated decision-making). This distinguishes it from sibling tools like breach_notification and dpia_generator.
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 explains what the tool does but does not explicitly state when to use it versus alternatives like lawful_basis_assessment or rights_request_handler. Usage context is implied but lacks direct guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as authorization requirements, rate limits, side effects, or failure modes. The 'caller' parameter hints at rate limiting but is insufficient for a complete behavioral picture.
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 structured with a summary sentence, then details, then args. It is slightly verbose by repeating parameters that could be omitted if schema had descriptions, but overall it is clear and well-organized.
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?
The tool is complex (8 params, output schema, GDPR-specific), and the description covers purpose and parameters well. However, it lacks behavioral details and usage guidance, leaving gaps about what the tool actually returns or how to handle edge cases.
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 0% description coverage, but the description includes an 'Args' section that explains each parameter (e.g., processing_purpose, data_categories, controller_type) with context. 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 clearly states the tool determines the appropriate lawful basis under GDPR Article 6, evaluating all six bases with AI-specific considerations. It distinguishes itself from sibling tools (breach_notification, classify_processing, etc.) which serve different compliance tasks.
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 implies usage for GDPR lawful basis assessment, but does not explicitly state when not to use or how it compares to siblings. The context from tool name and sibling list is clear enough, so it's adequate but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It describes the output structure but does not disclose non-obvious behaviors such as whether the tool stores or logs inputs, rate limiting details, or whether it requires authentication (caller and tier params hint but not explicit).
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 concise, starting with the main purpose in one sentence followed by a clear list of arguments. The argument list is well-structured but could be more integrated into the narrative. Overall efficient.
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 tool has 12 parameters, 5 required, and an output schema exists, the description adequately covers the primary functionality. However, it lacks details on error handling, default behaviors (beyond schema defaults), or prerequisites (e.g., needing prior lawful basis).
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%, meaning the schema provides no explanatory text. The description lists each argument with a brief purpose (e.g., 'Name of the AI system'), adding meaningful context beyond type and name. This compensates partially for the lack of schema documentation.
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?
Description clearly states it generates a Data Protection Impact Assessment per GDPR Article 35, and specifies it produces structured outputs including necessity assessment, risk evaluation, and mitigation measures. This distinguishes it from sibling tools like breach_notification or lawful_basis_assessment.
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?
Explicitly states the tool is required before high-risk AI processing begins, giving a clear trigger for use. However, it does not mention when not to use it or provide alternatives, though the sibling tools cover different compliance needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden of behavioral disclosure. It explains what the tool does (assess, determine, generate) and notes param roles (caller for rate limiting). However, it omits side effects (e.g., logging), authentication requirements, or rate limit specifics, leaving gaps in transparency.
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 moderately concise with a clear paragraph explaining purpose and action, followed by an Args list. The front-loading of purpose is effective, though the Args list partially duplicates the schema. Overall, it is well-organized and not overly verbose.
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 complexity of GDPR breach notification and the presence of an output schema, the description covers inputs and expected output (notification content). However, it could briefly mention output structure or any constraints (e.g., only works for specific jurisdictions) for fuller completeness.
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 must compensate entirely. It provides clear, detailed explanations for each parameter, including possible values for 'breach_type' (confidentiality, integrity, availability). This adds substantial meaning beyond the bare schema, enabling correct invocation.
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 specifies the tool's purpose: assessing breach severity and notification requirements under GDPR Articles 33-34, and generating notification content. It clearly differentiates from siblings like 'classify_processing' or 'dpia_generator' by focusing on breach-specific legal obligations.
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 when to use the tool (for breach assessment under GDPR) and references the 72-hour rule. However, it does not mention when to avoid using it or suggest alternative sibling tools, which would enhance guidance.
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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