Luxembourgish Data Protection MCP

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a clearly distinct purpose: getting guidelines vs. decisions, listing topics, searching guidelines vs. decisions, and retrieving server metadata. There is no overlap or ambiguity between search and get operations, and the topic listing tool serves as a distinct discovery mechanism.
Naming Consistency4/5
Tools use a consistent lu_dp_ prefix with get_/list_/search_ verbs followed by resource nouns (guideline, decisions, topics), forming a predictable pattern. Minor deviation: 'about' doesn't follow the verb_noun pattern, breaking the otherwise consistent convention slightly.
Tool Count5/5
Six tools is a well-scoped surface for a domain-specific data protection server. Each tool earns its place, covering discovery (about, list_topics), search (search_guidelines, search_decisions), and retrieval (get_guideline, get_decision) without redundancy.
Completeness4/5
The server covers the core workflows well: discovering topics, searching and retrieving guidelines, and searching and retrieving decisions with metadata like fines and GDPR articles. A minor gap is the lack of a 'get_topic' detail tool or direct lookup of decisions by entity name, but the full-text search covers most needs.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 present, so the description carries the full burden. It doesn't disclose what the returned document contains, whether it's a full text or summary, whether any authentication is needed, or any rate-limit/format considerations. For a retrieval tool with zero annotation coverage, this is a meaningful gap in behavioral disclosure.
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?
A single sentence that is direct and devoid of filler. Every word adds information — the actor verb, the resource type, the key, and the scoping detail. Zero waste.
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?
For a single-param, no-output-schema retrieval tool, the description is mostly adequate. The ID provenance hint, the clear verb, and the resource identification cover the essentials. However, without any annotations, additional context about what form the returned guidance takes (full text vs metadata) would materially improve completeness for agent decision-making.
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 coverage is 100% with a single well-described parameter. The description adds the sourcing hint that the ID comes from search_guidelines results, which enriches the schema's basic 'Guideline database ID' text by telling the agent where the ID originates. The 0-param-fix baseline is 4, and the description does provide useful provenance context for the ID value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb+resource ('Get' a 'CNPD guidance document') and specifies the retrieval key ('by its database ID'). It's not identical to siblings — search_guidelines obviously lists rather than fetches a single item — but it doesn't explicitly differentiate from get_decision or other retrieval tools beyond the resource type.
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 in the context of already having located a document's ID, and the parameter description mentions 'from lu_dp_search_guidelines results'. This creates an implicit workflow connection but doesn't explicitly state when to use this tool vs alternatives or when not to use it.
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 full burden. It doesn't disclose return format, pagination behavior, or whether results are ranked by relevance. It does clarify the query must be in French (a useful operational constraint) and lists topic coverage areas, but doesn't describe what happens for empty/no-match searches. With no annotations, this is adequate but not rich.
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?
Single sentence, front-loaded with the primary verb and resource, followed by concrete topic examples. Zero waste. Could arguably earn a 5, but the sentence is slightly dense on examples which could be trimmed.
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?
For a search tool with well-documented params and no output schema, the description covers the main axes: what it searches, the language requirement, and the topic scope. However, it doesn't clarify the relationship to lu_dp_get_guideline (does search return full docs or IDs for subsequent retrieval?) and lacks any notes about return structure or limits given no output schema exists.
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 all four parameters (query, type, limit, topic) are already documented in the schema. The description adds marginal value by explaining query should be in French and giving example values, and implicitly links the topic parameter to the listed domains. Baseline 3 is appropriate since the schema already does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Search') with a clear resource ('CNPD guidance documents') and lists concrete example topics (vidéosurveillance, cookies, sous-traitance). It distinguishes from sibling tools by positioning this as the search entry point vs. lu_dp_get_guideline (retrieval) or lu_dp_list_topics (topic enumeration), though it doesn't explicitly name the sibling to differentiate.
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 this is for searching guidance documents vs. lu_dp_search_decisions which presumably searches decisions, and mentions French queries explicitly. However, it doesn't state when to use this vs lu_dp_list_topics or lu_dp_get_guideline, nor any exclusions/alternatives. Usage context is inferred rather than 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?
No annotations are provided, so the description carries the burden of behavioral disclosure. The description usefully states it returns 'matching decisions with reference, entity name, fine amount, and GDPR articles cited,' which is helpful forward-looking context. However, it doesn't disclose pagination/limit behavior, case-sensitivity, relevance ranking, or what a zero-result response looks like.
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?
A single well-formed sentence that front-loads the core purpose and adds return value detail efficiently. No wasted words, though it could marginally tighten the parenthetical list of decision types which are partially restated in the type param enum.
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 search tool with 100% schema coverage and no output schema, the description conveys the core behavior (what is searched, what is returned). It's reasonably complete for a filtered-full-text-search tool, though it lacks explicit guidance on using topic/type filters together or result count handling, which the limit param implies.
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 schema already documents all 4 parameters and their meaning. The description adds the return-field context (reference, entity name, fine amount, GDPR articles) but doesn't add extra meaning about the parameters themselves beyond the schema, hence the baseline 3.
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 has a specific verb+resource ('Full-text search across CNPD decisions') and clearly distinguishes from siblings: it searches decisions (vs lu_dp_get_guideline which retrieves guidelines, lu_dp_search_guidelines which searches guidelines, and lu_dp_get_decision which retrieves a single decision). It enumerates decision sub-types and return fields, giving clear scope.
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 context (searching the CNPD decisions corpus) and the French-language requirement is explicit in the query param ('Search query in French'). However, it doesn't explicitly state when NOT to use this vs lu_dp_get_decision, which would be its closest sibling, though the verb split (search vs get) implies the distinction.
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 full burden of behavioral disclosure. The description implies a read operation (Get) but doesn't state whether this is non-destructive/read-only, whether it has permission requirements, what happens for invalid/malformed reference numbers, or what the response shape is. For a retrieval tool this is a notable gap.
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 with zero wasted words. Example formats are embedded inline for immediate usefulness. No redundant information or filler.
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?
This is a simple retrieval tool with a single well-documented parameter and output schema absent. The description sufficiently covers what the tool does and how to identify target decisions. It could note error behavior or return format, but for a basic single-param fetcher, the information provided is reasonably 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?
Schema coverage is 100%, with the single 'reference' parameter well-documented in the schema. The description adds value by providing example formats for the reference parameter beyond what the schema states, giving the agent concrete syntax patterns to use. However, it largely overlaps with schema content, so it doesn't exceed the baseline 3 by much.
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 verb (Get), resource (CNPD decision), and specific identifying mechanism (reference number). It also provides concrete example formats ('CNPD-2021-001', 'CNPD-DEC-2023-001') that distinguish it from sibling tools like lu_dp_search_decisions (search vs. direct fetch).
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 implicitly communicates when to use this tool — when you have a specific reference number and want a particular decision, as opposed to searching for decisions. It does not explicitly state 'use search_decisions for discovery, then this for retrieval', which would earn a 5, but the reference-based targeting is clear context.
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 full burden of behavioral disclosure. The description indicates the tool returns a listing of topics with bilingual names, which addresses the read-only nature implicitly. However, it doesn't disclose potential pagination, ordering, or whether the full list is always returned—though with 0 parameters, it's likely a complete static list. Acceptable but minimal 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?
Two efficient sentences, zero waste. The first states the core purpose and output format, and the second gives a practical hint about downstream usage (filtering by topic IDs). Well-structured and appropriately sized.
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 this is a zero-parameter list tool with no output schema, the description is reasonably complete. It explains what is returned (topics with both French and English names) and suggests a downstream use case. There's no output schema, so the description somewhat compensates by noting the bilingual names. Adequate coverage for a simple listing tool.
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 there is nothing to document. Per the rubric, 0 params yields a baseline of 4. The description correctly notes the output format (bilingual names), which adds value beyond the empty schema.
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 clear verb+resource pattern: 'List all covered data protection topics.' This is specific and unambiguous. While it doesn't explicitly contrast with siblings, the purpose is distinct from search/get operations among siblings, and the mention of bilingual names sharpens clarity.
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 states a clear use case: 'Use topic IDs to filter decisions and guidelines.' This implicitly tells the agent when to call this tool (i.e., likely as a prerequisite before using search/filter functions). However, it doesn't explicitly name alternatives or state when not to use it. The guidance is helpful but not fully explicit.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. For a metadata/about tool, the description clearly states what it returns (version, data source, coverage, tool list), which is effectively the behavioral contract for a read-only introspection tool. This is adequate disclosure for an about endpoint that doesn't mutate state or have side effects.
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 covers exactly what the tool returns with no wasted words. Front-loaded with the verb 'Return' and immediately enumerates the metadata categories. Every phrase earns its place.
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?
This is a zero-parameter about/metadata tool with no output schema, which makes it low complexity. The description enumerates the four categories of metadata returned (version, data source, coverage, tool list), which is sufficient for an agent to know what information it will get. No additional context about return format is strictly needed for such a simple introspection tool.
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 and the schema coverage is 100% (trivially, since the schema is empty). With no parameters to explain, the description has nothing to add beyond what the schema shows. Baseline 4 applies for zero-parameter tools.
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 states a specific verb ('Return') with a specific resource ('metadata about this MCP server') and enumerates the exact categories of data returned (version, data source, coverage, tool list). It clearly distinguishes itself from sibling research/retrieval tools (get_guideline, list_topics, search_guidelines) by being about the server itself rather than its domain content.
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 does not explicitly state when to use this tool versus alternatives. However, the purpose is self-evident: an 'about' endpoint is for server metadata, clearly distinct from sibling content-query tools. The usage context is strongly implied by the tool's nature, though no explicit alternatives or exclusions are named.
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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