Slovak Data Protection MCP

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource and action: search vs get for decisions, search vs get for guidelines, plus list_topics and about. There is no overlap or ambiguity between them.
Naming Consistency4/5
All tools share the 'sk_dp_' prefix and mostly follow verb_noun pattern (search_decisions, get_decision, get_guideline, search_guidelines, list_topics). The 'about' tool deviates slightly from the pattern but is still clear and consistent in style.
Tool Count5/5
With 6 tools, the server is well-scoped for a read-only data protection reference. Each tool covers a distinct aspect of the domain without unnecessary bloat or gaps.
Completeness5/5
The server covers both decisions and guidelines with search and retrieval, plus topic listing and metadata. For a read-only regulatory database, there are no obvious missing operations; users can find and retrieve relevant documents.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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This repository is licensed under Apache 2.0.
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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, so the description bears full responsibility for disclosing behavior. It only states the operation is 'Get', implying a safe read, but does not mention potential errors (e.g., ID not found), return format, permissions, rate limits, or any side effects. This is minimal behavioral transparency.
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 a single sentence, immediately states the action and the key qualifier, and contains no filler or redundancy. It is optimally concise and front-loaded.
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 simple with one parameter and a clear schema, but there is no output schema and no annotations. The description does not mention return value, error behavior, or any requirements, leaving some contextual gaps. It is minimally viable but not fully complete.
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 100% and the description merely restates the ID parameter as 'database ID', adding no meaning beyond what the schema already provides. The baseline of 3 applies because the schema handles parameter semantics effectively.
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 uses a specific verb+resource ('Get a specific ÚOOÚ SR guidance document') and identifies the exact scope ('by its database ID'). This clearly distinguishes it from siblings like sk_dp_search_guidelines (search) and sk_dp_get_decision (get decision).
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 is for retrieving a known guidance document by ID, and the schema hints that IDs come from sk_dp_search_guidelines results. However, it does not explicitly state when to use this tool versus alternatives nor include any exclusions, leaving usage guidance implied rather than explicit.
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, so the description must carry the full burden of behavioral disclosure. It only states the action ('Get') without elaborating on whether this is read-only, what happens if the reference is not found, or what the return value contains (e.g., full text vs. metadata). This lack of context is a significant gap for a tool with zero annotation coverage.
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 a single, front-loaded sentence that states the verb, resource, and the key identifier type, followed by examples. There is no wasted wording, making it highly efficient and clear.
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?
For a simple tool with one well-documented parameter and no output schema, the description provides the core purpose and parameter format. However, it lacks explicit usage guidance vs. siblings and does not describe the return value, which would be expected when no output schema exists. The description is adequate but not fully complete.
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?
The schema description for the 'reference' parameter already provides the same examples given in the tool description, so the description adds no additional value beyond the schema. With 100% schema coverage, the baseline of 3 is appropriate.
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 gets a specific ÚOOÚ SR decision by reference number, with concrete examples ('UOOU-2021-1234', '00946/2022'). This distinguishes it from sibling tools like search_decisions (which presumably searches) and get_guideline (different resource).
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 when you have a specific reference number, providing clear context. However, it does not explicitly mention alternatives or when not to use this tool, such as pointing to sk_dp_search_decisions when the reference is unknown. The sibling names help, but the description itself offers no exclusions.
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 are provided, so the description carries the burden of disclosing behavioral traits. It indicates full-text search and lists returned fields, which is useful context. However, it does not mention that the operation is read-only, how pagination behaves, or any limitations such as language or result ordering. It adds some behavioral details but lacks depth.
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 sentences, front-loaded with the main purpose and a concise list of returned fields. Every word contributes to understanding, with no fluff or redundant information.
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 there is no output schema, the description sufficiently explains the return values (reference, entity name, fine amount, articles) and the search scope. Parameters are fully described in the schema. It doesn't mention error cases or pagination details, but the schema includes the limit default, so it is complete enough for a search tool.
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?
The input schema provides full descriptions for all 4 parameters (100% coverage), so the baseline is 3. The description does not add extra meaning about parameters; it only mentions output fields. Schema already explains query, type, limit, and topic, making additional description unnecessary.
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 'Full-text search across ÚOOÚ SR decisions' with specific decision types (rozhodnutia, pokuty, upozornenia) and enumerates returned fields (reference, entity name, fine amount, GDPR articles). It distinguishes this from siblings like sk_dp_get_decision (specific decision retrieval) and sk_dp_search_guidelines (guideline search).
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 searching decisions but does not explicitly state when to use this tool over alternatives such as sk_dp_get_decision or sk_dp_search_guidelines. No exclusion criteria or alternative tool recommendations are provided, so usage context is implied rather than explicit.
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, so the description carries the burden of behavioral disclosure. It does not mention any behavioral traits such as pagination, sorting, output format, or whether the search is read-only. The description focuses on content scope but omits operational details, leaving the agent with limited understanding of side effects or response characteristics.
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 a single, concise sentence that front-loads the core purpose. It includes useful examples without unnecessary fluff, making it efficient and well-structured.
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 tool's simplicity, the description adequately covers the search scope and examples. Since there is no output schema, it does not need to detail return values. However, it could mention that results are likely a list of matching documents with metadata, but the absence of that is a minor gap.
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 100%, so the baseline is 3. The description itself does not add much beyond what the schema already provides, though it does align with the query examples. It does not enhance understanding of the 'type' or 'topic' parameters beyond schema definitions.
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 searches ÚOOÚ SR guidance documents, with a specific verb ('Search') and resource ('guidance documents'). It also lists specific types (usmernenia, stanoviská, metodické pokyny) and example topics, making its purpose distinct from sibling tools like search_decisions.
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 about what it searches (guidance documents) and includes examples of topics, implying appropriate use cases. However, it does not explicitly state when to use this tool over siblings (e.g., 'for decisions, use search_decisions'), missing an opportunity for differentiation.
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 the full burden. It mentions the output includes topic names and implies IDs, but does not disclose details like pagination, authorization, or exact return format. For a simple list operation, this is 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with no waste. The main purpose is front-loaded, and the additional usage hint is valuable without being 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?
The tool is simple, with no parameters and no output schema. The description covers what it lists (topics with names) and how to use the result (IDs for filtering), which is sufficient for this 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?
The tool has zero parameters, so the description does not need to explain parameter usage. The baseline for zero-parameter tools is 4, and the description appropriately focuses on the tool's purpose and output.
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 lists all covered data protection topics and specifies that it includes Slovak and English names. This is a specific verb+resource combination that distinguishes it from sibling tools that search or retrieve decisions and guidelines.
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 second sentence tells the agent to use topic IDs to filter decisions and guidelines, implying this tool is the source for those IDs. It provides clear context for when to use this tool, though it does not explicitly state alternatives or exclusions.
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?
With no annotations provided, the description clearly states the tool's output and behavior. It is a read-only metadata operation with no side effects disclosed, which is appropriate for its simple nature.
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 a single, well-structured sentence that front-loads the core purpose and lists the included metadata elements without waste.
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 simple metadata tool with no parameters and no output schema, the description fully covers what the tool does and what it returns. It is adequately complete.
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 the schema coverage is 100%. The description adds no parameter details because none exist, but the baseline for 0 params is 4.
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's function: returning metadata about the MCP server, including version, data source, coverage, and tool list. This distinguishes it from sibling tools that handle decisions or guidelines.
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 purpose is self-evident as a server info tool, and the context implies when it would be used. However, it does not explicitly mention alternatives or exclusions, though none are really needed.
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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