Xearno Tools

India Gratuity Calculator (Labour Codes, from 21 Nov 2025)

india_gratuity

Read-only

Statutory gratuity under the new Labour Codes — the 50% wage floor and the 1-year fixed-term gate that the old answer misses. Computes statutory gratuity under India’s Code on Social Security 2020, in force since 21 Nov 2025 (general AI often still says the Labour Codes are pending). The formula looks unchanged — wages × 15/26 per year of service — but two decisive inputs are hidden: the §2(88) wage definition floors the gratuity base at 50% of total remuneration when basic + DA is kept low (most modern salary structures), and fixed-term employees now qualify after just 1 year instead of 5. Both can turn the “obvious” answer from wrong to right by lakhs.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`basicDA`	No	Monthly basic + DA last drawn Basic pay + dearness allowance (+ retaining allowance, if any) in your last drawn month. Gratuity runs on last-drawn wages, not an average.
`exitReason`	No	Why is employment ending? Death or disablement waives the qualifying-service requirement entirely; the formula is otherwise the same.	service
`serviceYears`	No	Completed years of service Whole completed years of continuous service. Put the leftover months in the next field.
`employmentType`	No	What kind of employment? This gate is the headline change — fixed-term employees now qualify after just 1 year (pro-rata), and working journalists after 3. Under the old Act the answer for a 2-year fixed-term worker was simply ₹0.	permanent
`totalRemuneration`	No	Total monthly remuneration Everything monthly: basic, DA, HRA, allowances, employer PF contribution, statutory bonus — but not gratuity or ESI (per the MoLE FAQ). The 50% floor: if basic + DA is under half of this, the law adds the excess back. THIS is what most people don’t know.
`serviceExtraMonths`	No	…plus months Months beyond the completed years. More than 6 months rounds UP to a full extra year (§53(2)); exactly 6 does not.

TDQS

A4.4/5.0

Behavior5/5

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

The description adds significant behavioral context beyond the annotations (readOnlyHint=true). It details the wage floor calculation, the 1-year gate for fixed-term employees, the rounding rule for months, and the impact of modern salary structures. No contradiction with annotations.

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 relatively long but well-structured, starting with the key takeaway about the new law and then elaborating on hidden inputs. Every sentence contributes value, though it could be slightly more concise without losing clarity.

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

Completeness3/5

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

No output schema is provided, and the description does not mention what the tool returns (e.g., total gratuity amount). While the name implies a calculation result, the lack of explicit output description leaves a gap in completeness for an otherwise detailed description.

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% with each parameter having a description. The tool description further explains the hidden nuances (e.g., the 50% floor, the effect of employment type) that go beyond the schema, adding substantial value. Baseline 3, adjusted up for extra context.

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 computes statutory gratuity under India's Code on Social Security 2020, effective 21 Nov 2025. It uses specific verbs ('computes') and resources ('statutory gratuity'), and distinguishes itself from outdated answers by highlighting the new law and hidden inputs.

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 provides context for when to use the tool: for gratuity calculations under the new Labour Codes, especially emphasizing the 50% wage floor and fixed-term employee qualification. However, it does not explicitly state when not to use it or mention alternative tools (e.g., the old Act, other gratuity calculators).

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

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TDQS

A3.9/5.0

Disambiguation4/5

Each tool targets a distinct niche (e.g., specific country tax rules, loan types, or legal calculations), with detailed descriptions that clarify boundaries. However, the large number of tools (66) could cause some confusion for an agent trying to select the right one for a general query, especially when multiple tools relate to the same country.

Naming Consistency4/5

Tool names follow a mostly predictable pattern: lowercase words separated by underscores, often starting with a country name (e.g., 'uk_stamp_duty_sdlt') or a topic (e.g., 'compound_growth'). There are minor deviations, such as abbreviations ('npv_irr', 'sip') and varying use of verbs, but overall the naming is clear and consistent.

Tool Count3/5

At 66 tools, the server is unusually large and covers an extensive range of financial and legal calculators. While each tool justifies its existence, the count exceeds the typical well-scoped range (3–15), making the server feel bloated. A more modular design might improve coherence.

Completeness4/5

The tool set covers a wide array of domains: personal income taxes, property taxes, loan calculations, investment returns, and specific country regulations. Minor gaps exist (e.g., missing tools for corporate taxes, general retirement planning, or insurance), but the overall coverage is thorough and addresses many niche scenarios that general AI handles poorly.

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