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Glama

Read a consolidated text

leginova_get_texte_consolide
Read-onlyIdempotent

Retrieve New Caledonian consolidated legal texts in force, including metadata, article tables, full text by chunk, single articles, and amendment histories.

Instructions

Reads a consolidated text (loi du pays, délibération, arrêté... in its current version with all amendments applied). mode "outline" returns metadata, the table of articles and the amendment history; mode "full" returns the text itself, chunked by max_chars (pass back offset to continue). Pass article_id to read a single article with its own history. Always state the consolidation date when quoting: the text is the version in force on that date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesConsolidated text id (texteConsolideId in search results)
modeNofull
offsetNo
max_charsNo
article_idNoRead only this article
include_historyNoAppend the list of amending acts

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/openWorld, so the bar is lower, but the description adds real behavioral context: chunked pagination via max_chars/offset, per-mode return shapes, and the important warning that the text is the version in force on the consolidation date and that date must be quoted.

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?

Three tight sentences, front-loaded with the core action and the mode semantics, then the pagination mechanic and the citation caveat. Dense but every clause carries information; no 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?

With no output schema and six parameters, the description does the heavy lifting by describing what each mode returns and how paging works. The only modest gap is that include_history's effect is left entirely to the schema's short note.

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 only 50%, so the description must compensate, and it does for the key parameters: it explains what each mode returns, that max_chars chunks the output, that offset continues the read, and that article_id scopes to one article with its history. Only `include_history` and `id` are left to the schema.

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?

Clearly states a specific verb and resource ('Reads a consolidated text') and disambiguates from the code-oriented siblings by naming the legal source types (loi, délibération, arrêté). It also spells out the two operating modes, so the agent knows the tool's shape, though it never explicitly contrasts itself with leginova_get_code_article or leginova_read_code_section.

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?

Gives clear selection guidance between mode 'outline' and 'full', explains when to pass article_id, and instructs the agent to pass back `offset` to continue paging. It lacks any explicit when-not or named-alternative routing, but the context is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.