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ru-payroll-mcp

МРОТ, предельные базы и ставки

payroll_params
Read-onlyIdempotent

Get payroll reference data for 2025 or 2026: minimum wage, tax rates, contribution limits, and sick leave parameters. Use to answer rate questions or verify calculations.

Instructions

Справочные параметры для зарплаты за 2025 или 2026 год: МРОТ, предельная база страховых взносов, 1,5 МРОТ для льготы МСП, шкала НДФЛ, тарифы взносов и максимальный средний дневной заработок для больничных. Используйте, чтобы ответить на вопрос о ставках или проверить исходные данные. Только чтение.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoРасчётный год: 2025 или 2026 (по умолчанию текущий)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is fully covered; the closing "Только чтение" merely repeats it and earns no credit. The description does add genuinely useful behavioral context by scoping the data to the 2025/2026 reference years, but says nothing about freshness, source, or limits.

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 two dense sentences plus a short tag; the content enumeration is front-loaded and earns its place. The trailing "Только чтение" is redundant given the annotations and is the only wasted token.

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?

With no output schema, the description correctly compensates by enumerating the values returned, and the single parameter is fully covered by the schema. Annotations handle the safety profile, so nothing an agent needs to call this lookup tool is missing; only data freshness/latency context is absent.

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 `year` parameter is fully documented in the schema, so the baseline is 3. The description corroborates the valid range ("за 2025 или 2026 год") but adds no format or behavioral detail beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource (payroll reference parameters) and enumerates exactly what it returns: МРОТ, insurance contribution base limits, 1.5 МРОТ for the SME benefit, NDFL scale, contribution tariffs, and maximum average daily earnings. This implicitly separates it from the calculator siblings (salary_calc, ndfl_calc, etc.), though none of them is named explicitly.

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?

"Используйте, чтобы ответить на вопрос о ставках или проверить исходные данные" gives two clear triggering scenarios: answering rate questions and verifying source data. There is no explicit when-not guidance or named alternative, but the context is unambiguous.

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