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tax-retirement

by io.github.herve-coulon

retraite_ps_pension

Prélèvements sociaux sur une pension (brut → net) — CSG (taux 0/3,8/6,6/8,3 % selon le RFR et les parts) + CRDS + CASA + 1 % maladie sur la part de pension complémentaire. Rend la pension nette, la tranche CSG et le taux effectif. Barème 2026 (seuils RFR 2024). (sources: CSS art. L136-8 (CSG pensions) ; CSS art. L131-2 (1 % maladie retraite complémentaire) ; Barème CSG des pensions 2026 (seuils RFR 2024, circulaire CNAV 22/12/2025))

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rfrNoRevenu fiscal de référence 2024 du foyer (€) — détermine la tranche CSG. Si absent, la pension sert de proxy et la tranche est « estimée ».
partsNoParts fiscales du foyer (interpolation linéaire des seuils entre 1 et 2 parts).
montant_complementaireNoPart de la pension relevant d'un régime complémentaire (Agirc-Arrco, Ircantec, CRPN, comp. libéraux…), soumise en plus au 1 % maladie aux tranches médiane/normale. Défaut 0 = base seule.
pension_annuelle_bruteYesPension annuelle brute totale (€).

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains the gross-to-net transformation, the taxes included, the returned values, the applicable year, and legal sources. It does not cover edge cases, but for a stateless calculation tool this is strong transparency.

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 dense but well organized: scope, calculation components, outputs, applicable barème, and legal sources all appear without repetition. The core purpose is front-loaded in the first clause, and every clause contributes useful information.

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 calculation tool with no annotations and no output schema, the description is complete: it states required inputs, optional contributing factors, tax logic, output values, year, and regulatory references. Combined with the fully described parameter schema, an agent has enough context to invoke it correctly.

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 100%, so the baseline is 3. The description adds value beyond the schema by linking montant_complementaire to the 1% maladie on the complementary pension share and by specifying CSG rate tiers and the RFR/parts dependency.

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 opens with a precise action and resource: 'Prélèvements sociaux sur une pension (brut → net)', then enumerates the exact components (CSG, CRDS, CASA, 1% maladie) and outputs (pension nette, tranche CSG, taux effectif). This clearly differentiates it from broader fiscal/retirement sibling tools.

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 clearly frames when the tool applies: computing social contributions on a pension using RFR, parts, and complementary pension share, under the 2026 scale. It does not explicitly name alternative sibling tools or exclusion conditions, but the usage context is unambiguous.

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

All tax-retirement toolsInspect
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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