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manganate006

OpenLMNP

Get Fiscal Year

get_fiscal_year
Read-only

Fetch complete fiscal year details by year, including fiscal result, income, expenses, depreciation, opening balances, VAT, and tax form data for 2031/2033.

Instructions

Retourne le détail complet d'un exercice fiscal par son année : résultat fiscal, recettes, charges, amortissements (total, plafonné, reporté), soldes d'ouverture d'une reprise de dossier (amortissements différés, déficits par millésime, cumul d'amortissements de contrôle, provenance), TVA, form_data du formulaire 2031/2033, et informations de télétransmission.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description adds meaningful behavioral context by itemizing the complete payload: tax result, revenues, expenses, depreciation components, opening balances, VAT, form_data, and filing information. It does not mention behavior for missing years or invalid input, but for a read-only detail endpoint that is a minor gap.

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 a single front-loaded sentence that names the action and object first, then uses a compact parenthetical list to convey return contents. Every phrase adds information because there is no output schema, and there is no filler or repetition.

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 one-parameter read-only tool with no output schema, the description explains the return payload in enough breadth to let an agent judge whether this tool fits the task. It is slightly incomplete because it does not contrast the tool with compute_fiscal_year/list_fiscal_years or describe error conditions, but the core calling context is well covered.

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 schema only provides the parameter name and type. The description adds "par son année," confirming that year identifies the fiscal year, which is helpful for the single required parameter. It still does not specify the expected format, bounds, or behavior when the fiscal year is absent.

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 and resource: "Retourne le détail complet d'un exercice fiscal par son année." It also enumerates the returned content domains, making the tool's scope far more concrete than just its title. The phrase "détail complet" distinguishes it from list-oriented siblings like list_fiscal_years.

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

Usage Guidelines3/5

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

The description implies the tool is for retrieving full detail for an existing fiscal year, and the readOnlyHint reinforces that it is a safe read operation. However, it never explicitly says when to prefer this over list_fiscal_years or compute_fiscal_year, leaving routing partly to inference from sibling names.

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