Skip to main content
Glama

fiscal_prelevements_sociaux

Taux de prélèvements sociaux par nature de revenu — Taux de PS applicable (17,2 % dérogatoire ou 18,6 % droit commun post-LFSS 2026) selon la nature du revenu du capital. Renvoie une FRACTION 0-1 (unit RATE, ex. 0,172) + un champ "pourcentage" (17,2) — à distinguer de tmi qui renvoie des points de pourcentage (unit PERCENT, 0-100). (sources: LFSS 2026 art. 12 (loi 2025-1403) ; CSS art. L.136-1 s.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
type_revenuYesNature du revenu (ex. rf_location_nue, rcm_dividendes, bic_lmnp).

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses the exact return format (fraction 0-1 and 'pourcentage' field), the applicable rates (17.2% vs 18.6%), and the legal sources. It does not cover edge-case behavior, but that is a minor gap for a simple lookup tool.

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 compact and well organized: the purpose comes first, followed by rates, return format, the distinction from tmi, and sources. There is no redundant content.

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 a tool with one enum parameter and no output schema, the description tells an agent what input is needed, what output to expect (fraction and percentage), what units to use, and which legal references apply. The agent has enough information to select and invoke the tool correctly without further lookup.

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 100%, and the single parameter 'type_revenu' is already fully documented by the enum with legitimate values and an example. The description adds the contextual notion of 'revenu du capital' but does not need to restate parameter details the schema already handles.

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 identifies the tool's purpose: returning the French social contributions rate (taux de prélèvements sociaux) based on the nature of capital income. It names specific rate values, distinguishes this from the sibling 'fiscal_tmi' tool, and makes clear it returns a rate not tax points.

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 warns 'à distinguer de tmi' and explains that tmi returns percentage points (unit PERCENT, 0-100) while this tool returns a fraction (unit RATE) plus a percentage field. This gives an explicit routing decision: use this tool for PS rates and the other one for TMI points.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation4/5

The tool families are clearly separated by domain prefixes (fiscal, retraite, referentiel), and the descriptions proactively distinguish near-neighbors such as fiscal_tmi vs fiscal_prelevements_sociaux and retraite_estimation vs retraite_pension_totale. Some initial confusion is possible between retraite_pension_regime and retraite_regimes, but the descriptions are detailed enough to resolve it.

Naming Consistency4/5

Most tools follow a predictable snake_case domain_object pattern: fiscal_*, retraite_*, referentiel_*. The clear outlier is qotien_capacites, which uses a misspelled, non-domain prefix and breaks the otherwise consistent naming scheme.

Tool Count3/5

With 18 tools, the server is heavier than the typical 3-15 sweet spot, though it is organized into recognizable fiscal, retirement, and referential clusters. The count is defensible for a two-domain server, but it begins to feel like a large MCP surface that agents must navigate carefully.

Completeness4/5

The server covers the main French income-tax and retirement calculation needs: income tax, marginal rate, social levies, CEHR, property capital-gains surtax, pension estimation, multi-regime totals, net pension, buybacks, progressive retirement, and parental surcharge. Minor gaps exist—such as no explicit long-career early-retirement tool and pensions_fournies being referenced rather than exposed as a first-class tool—but agents can work around them.

Resources