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fedlex.get_structure

Read-only

Outline one consolidation (sections, articles with eId, num and heading): use when you know the act but not the article number. norm.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoManifestation language (de|fr|it|en|rm); default de. Which languages a version carries as XML is the graph's answer — fedlex.list_expressions shows it before a read.
depthNo«article» (default: the skeleton down to articles) or «full» (the whole tree down to paragraphs and items).
eli_versionYesDated consolidation: `<abstract-eli>/<YYYYMMDD>`.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already mark this as read-only, and the description adds useful context about what the outline includes and how the depth parameter changes the result from a skeleton down to articles versus a full tree down to paragraphs. It does not contradict the readOnlyHint annotation.

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 sentence front-loads the tool's purpose and usage context efficiently. The trailing 'norm.' is unexplained and adds minor noise, preventing a perfect score.

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 read-only outline tool with three well-documented parameters and no output schema, the description supplies enough context: what is returned, the depth variants, and when to use it. The schema covers the remaining input semantics, so the overall package is sufficiently 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 schema already documents lang, depth, and eli_version in sufficient detail. The description itself does not meaningfully add parameter-level meaning beyond what the schema provides, so 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 a specific verb ('Outline'), a concrete resource ('one consolidation'), and the content delivered ('sections, articles with eId, num and heading'). It also differentiates the tool from article-level lookups by specifying the scenario 'when you know the act but not the article number.'

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 gives an explicit usage trigger: 'use when you know the act but not the article number,' which implies the alternative of a more targeted article-reader when the article number is known. It stops short of naming sibling tools or stating explicit when-not-to-use cases, so it is not a perfect 5.

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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TDQS

A3.5/5.0
Disambiguation4/5

The tools are strongly namespaced and most have a clear 'use for' hint, so an agent can usually select correctly. A few near-neighbour pairs, such as fedlex.get_citations vs fedlex.get_references and facts.badge vs facts.latest, require careful reading but are still distinguishable.

Naming Consistency4/5

The <domain>.<snake_case_action> style is consistent and the fedlex/lindas families are predictable. However, several noun-style names such as meta.tools, lindas.observations, and facts.badge deviate from the verb_noun pattern, and the variety of get_/read_/list_/find_ verbs adds minor noise.

Tool Count2/5

With 52 tools, this is a very large MCP surface for one server; while each tool appears individually purposeful, the combined set is heavy for an agent to explore and select from. The gateway would be more manageable split into separate legal, data, and meta servers.

Completeness5/5

The Fedlex cluster covers the full legal-research workflow: search, version resolution, reading, comparison, citation checking, history tracing, consultations, and official publications. The LINDAS cluster covers cube discovery, schema inspection, filtering, and label resolution, and the directory/meta tools complete the capability-discovery loop. There are no obvious dead ends or critical missing operations.

Resources