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up-law-argleg-mcp

up-law-argleg-mcp

by UPDerechoIA
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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leer_articulos

Fetch exact article texts from Argentine legal norms with verifiable provenance, supporting selection by article numbers or document version.

Instructions

Texto LITERAL de articulos de una norma, con proveniencia por articulo. articulos: "245", "240-250" o "14 bis, 245". Sin articulos devuelve todos. Cita el texto de forma textual y verifica cada cita con verificar_cita antes de responder. documento: texto_actualizado | texto_original (default: el actualizado si existe).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ref": "<ref>"
    },
    "name": "leer_articulos"
  }
}

Input Schema

NameRequiredDescriptionDefault
refYes
articulosNo
documentoNo
fragmentoNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

No annotations are provided, so the description must disclose behavior. It reveals that the tool calls verificar_cita to verify citations before responding, and indicates it returns literal text. No mention of destructive actions, auth, or rate limits, but for a read-like tool this is adequate.

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 reasonably compact, using backtick formatting for parameters and a clear instruction. It is slightly verbose with line breaks but remains concise enough for an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description lacks details on return format and the fragmento parameter. It covers articulos and documento well but omits ref semantics and output structure. Overall adequate but with notable gaps.

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 0%, so the description must add meaning. It explains articulos format (e.g., '245', '240-250') and default, and documento options (texto_actualizado/texto_original) with default. However, ref and fragmento parameters are not explained, leaving gaps.

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 'Texto LITERAL de articulos de una norma, con proveniencia por articulo' which clearly defines the tool's function: retrieving literal article text with provenance. It also specifies behavior when articulos is omitted and differentiates from sibling tools like buscar_articulos.

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 guidelines: 'Sin articulos devuelve todos' indicates when to omit parameter, and 'Cita el texto de forma textual y verifica cada cita con verificar_cita antes de responder' gives a specific usage instruction. However, it does not explicitly compare with siblings or state when not to use.

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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