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fedlex.get_taxonomy

Read-only

Classify an act in the systematic collection (SR branch chain, notation, labels de/fr/it): use for «zu welchem Rechtsgebiet gehört X?». norm.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eliYesFedlex ELI of the consolidation abstract.

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 declare readOnlyHint=true, lowering the bar for safety disclosure. The description adds useful behavioral context by indicating the tool produces a classification consisting of SR branch chain, notation, and trilingual labels, which is beyond what a bare 'classify' would convey.

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 compact and front-loads the core purpose, then gives a concrete usage hint. The trailing 'norm.' is slightly cryptic and could confuse, which prevents 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?

With only one well-documented parameter and a readOnly annotation, the description covers the essential selection and invocation context. It also notes the return categories enough to set expectations despite lacking an output schema. Minor ambiguity around 'norm.' and exact output format prevents a higher score.

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 'eli' parameter is already described as 'Fedlex ELI of the consolidation abstract.' The tool description does not add further parameter detail, so the schema carries the semantic weight; this meets the baseline.

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 action ('Classify an act in the systematic collection') and clearly identifies the relevant output aspects (SR branch chain, notation, labels de/fr/it). The quoted German question 'zu welchem Rechtsgebiet gehört X?' makes the intended use immediately recognizable and distinguishes it from other Fedlex tools.

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?

It gives an explicit use case via the question 'zu welchem Rechtsgebiet gehört X?', which tells the agent when to invoke this tool. It does not explicitly mention exclusions or alternatives, but the intent is clear enough for selection among many sibling tools.

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.

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