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justicelibre

by io.github.Dahliyaal

resolve_law_number

Read-only

Résout un numéro de loi/ordonnance/décret vers son identifiant LEGITEXT ou JORFTEXT Légifrance.

Utile pour les textes non codifiés (lois, ordonnances, décrets) qui ne
sont pas dans la liste des ~80 sigles courts (CC, CP, COJ, LIL, etc. — resource `justicelibre://codes-supportes`).
Une fois le LEGITEXT/JORFTEXT résolu, on peut l'utiliser avec
`get_law_article(code=<LEGITEXT>, num=<N>)` pour récupérer un article
spécifique.

Exemples :
- `resolve_law_number("68-1250")` → loi prescription quadriennale des
  créances publiques (JORFTEXT000000878035)
- `resolve_law_number("79-587")` → loi motivation des actes admin
- `resolve_law_number("2000-321")` → loi droits citoyens face à l'admin

Args:
    numero: format "YY-NNNN" ou "YYYY-NNNN" (ex: "68-1250", "2000-321")

Returns:
    `{numero, legitext, titre_texte, date_debut, articles_count, source_url}`
    ou `{error}` si introuvable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
numeroYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior4/5

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

The readOnlyHint already signals a safe read operation, and the description adds useful behavioral detail: the tool returns a structured object including article count and source URL, and returns {error} when not found. This goes beyond the annotation and gives the agent expectations about failure modes.

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 well-structured and front-loaded: what the tool returns, when to use it, how to use it after, and concrete examples. Every sentence earns its place, and the parameter and return format sections are scannable.

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 an input schema with a single undocumented string parameter, the description covers input format, output shape, error case, supported text types, and integration with related tools. Nothing important is missing for an agent to select and invoke this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must fully explain the lone parameter, and it does: it specifies the expected format 'YY-NNNN' or 'YYYY-NNNN' with concrete examples. The examples (68-1250, 79-587, 2000-321) make the input semantics unambiguous.

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 — resolving a law/ordonnance/décret number to a LEGITEXT or JORFTEXT identifier — and clearly distinguishes it from the many search/get sibling tools. It also clarifies the scope (non-codified texts, not the ~80 short codes), so an agent can tell this tool apart from search_legi or get_law_article.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says this is useful for non-codified texts that are not in the list of ~80 short codes, which is a clear when-to-use signal. It also explains the follow-up workflow with get_law_article, giving the agent a concrete integration path and an implicit alternative.

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

A4.1/5.0
Disambiguation4/5

The tools are mostly distinct by legal source (admin, CC, CEDH, CJUE, etc.) and by action (search vs. retrieve). However, pairs like search_admin vs. search_conseil_etat both target Council of State jurisprudence, and get_decision_text could be mistaken for a generic decision retriever, creating some potential confusion despite detailed descriptions.

Naming Consistency5/5

All tool names follow a consistent lowercase snake_case pattern with action-first naming (get_, search_, list_, build_, resolve_, about_). The get_decision_* and search_* prefixes clearly separate retrieval from search, and modifiers like _libre and _recent are applied systematically.

Tool Count2/5

With 31 tools, the server is heavy, exceeding the 25+ threshold for 'too many.' While the breadth of French legal sources justifies many search/retrieval tools, the three recent admin variants and the redundant PISTE vs. libre duplicates suggest the set could be consolidated. The count feels disproportionate to the core purpose of legal research.

Completeness4/5

The tool set covers the full read lifecycle for legal research: orientation (about), search across all major sources, full-text retrieval for each source, law article lookup with historical versions, and URL building. Minor gaps exist, such as lacking a structured code browse or a direct way to enumerate all articles in a section, but these are workarounds via search_legi and get_law_article.

Resources