Skip to main content
Glama

calculer_indemnites_licenciement

Calculates legal severance pay under French labor law based on seniority, reference salary, and reason. Includes tax regime.

Instructions

Calcule l'indemnité légale de licenciement selon le Code du travail. Prend en compte l'ancienneté, le salaire de référence, et le motif. Inclut le régime fiscal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
salaireBrutMensuelYesSalaire brut mensuel moyen des 12 derniers mois
salaireBrutMensuelAvecPrimesNoSalaire brut mensuel moyen des 3 derniers mois (primes incluses au prorata)
ancienneteAnneesYesAnnées complètes d'ancienneté
ancienneteMoisNoMois supplémentaires d'ancienneté (0-11)
motifYesMotif de la rupture
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool calculates legal indemnity and includes tax regime, but does not mention behavioral traits such as being read-only, requiring specific authorization, or handling edge cases. For a calculation tool, this is adequate but not comprehensive.

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 three sentences, front-loaded with the main action ('Calcule...'), and each sentence adds essential information. It is concise and to the point with no wasted words.

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

Completeness2/5

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

The tool has no output schema and the description does not explain the return format (e.g., calculated amount, breakdown). For a legal calculation tool with 5 parameters, the agent needs to know what output to expect. The description lacks this critical information.

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?

The input schema has 5 parameters, all with descriptions (100% coverage). The description adds context ('according to French Labor Code, includes tax regime') but does not provide details beyond what the schema already states. With high schema coverage, baseline is 3, and the description adds marginal value.

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 clearly states the tool calculates legal severance pay according to the French Labor Code, specifying it accounts for seniority, reference salary, reason, and tax regime. This is a specific verb+resource, distinguishing it from sibling tools like 'calculer_indemnites_conges' (vacation pay) and others that deal with different calculations.

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 usage for calculating legal severance but does not explicitly state when to use this tool vs alternatives. No exclusion criteria or alternative tools are mentioned, leaving the agent to infer usage from the tool's name and purpose.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mcp-tools-lab/french-admin-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server