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SFHAJJI

Lex: Luxembourg Law MCP Server

cited_by

Find which articles cite a specific law, including amendments and references. Reverse-search legal cross-references to see what amended it, what depends on it, and what still refers to it.

Instructions

Which ARTICLES point at this law. The reverse of the cross-references the publisher writes into its own text ("modifie par la loi du 4 juin 2020"), captured at derive time. Answers "what depends on this law", "who amended it", "is anything still referring to it" — the question legal research is actually made of, and the one a search box cannot answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workYesthe law being cited, e.g. lu-legilux:loi-2020-06-04-a476
limitNodefault 50
Behavior4/5

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

No annotations exist, so the description carries the full burden. It discloses a key behavioral trait: "captured at derive time," indicating the data is a snapshot rather than real-time. It also explains the underlying mechanism (reverse of publisher cross-references), giving insight into what results include. However, it does not explicitly state read-only behavior or response format, though the query-like semantics imply safety.

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 two sentences, front-loaded with the core purpose. The second sentence adds valuable behavioral and usage context, though the closing phrase "the question legal research is actually made of, and the one a search box cannot answer" is a rhetorical flourish that could be trimmed. Overall, it is appropriately sized 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 lack of output schema and annotations, the description does a good job explaining the tool's purpose, behavior, and use cases. It clearly indicates the output is a list of citing articles, but does not specify exact response fields or pagination details. For a moderate-complexity lookup tool, this is nearly complete but could be enhanced with response structure notes.

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 100% (both 'work' and 'limit' are described in the schema), so the baseline is 3. The description adds conceptual context about what "cited" means, but does not add parameter-specific details beyond the schema, such as how limit behaves or whether work accepts multiple formats.

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 opens with "Which ARTICLES point at this law," a specific verb+resource pairing that clearly states the tool's function. It distinguishes itself from search by noting "the one a search box cannot answer" and differentiates from sibling tools by emphasizing the reverse nature of citations.

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 explicit use cases: "answers 'what depends on this law', 'who amended it', 'is anything still referring to it'." It implicitly contrasts with search, but does not explicitly mention sibling alternatives or exclusion criteria, so it has clear context without formal exclusions.

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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