Xearno Tools

Thailand Social Security Contribution (2026 ceiling unfreeze)

thailand_social_security

Read-only

Your monthly SSO contribution under the 2026 ceiling rise — the ฿15,000 cap stood for 30 years, so the old ฿750 answer is everywhere and wrong. Computes your Thai Social Security Office (SSO) contribution for Section 33 (employees), Section 39 (voluntary ex-employees), or Section 40 (informal workers). The Section 33 wage ceiling was frozen at ฿15,000/month from 1995 until the Royal Gazette announcement of 12 Dec 2025 raised it to ฿17,500 from 1 Jan 2026 — so the maximum employee contribution jumps from ฿750 to ฿875, with further phases to ฿20,000 (2029) and ฿23,000 (2032). Thirty years of the old number mean general AI and much of the Thai web still answer ฿750; this tool uses the phased schedule, and knows §39 stays on its frozen ฿4,800 base.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No	Contribution year The ceiling rises in three phases: ฿17,500 (2026), ฿20,000 (2029), ฿23,000 (2032). Pick 2025 to see the old frozen ceiling.	2026
`section`	No	Which SSO section are you under? The section decides everything: §33 is percentage-of-wage with a ceiling, §39 is a flat ฿432, §40 is a chosen flat option.	33
`s40option`	No	Section 40 option Only used for Section 40. Higher options buy more benefit branches.	1
`monthlyWage`	No	Monthly wage (฿) Gross monthly wage — used for Section 33 only. Contributions apply between the ฿1,650 floor and the year’s ceiling.

TDQS

A4.7/5.0

Behavior5/5

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

Annotations show readOnlyHint=true, and the description adds extensive behavioral context: historical freezing, phased ceiling increases, and that general AI answers are outdated. No contradictions.

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 somewhat verbose with historical context, but it is well-structured, front-loads the key change (2026 ceiling unfreeze), and every sentence adds relevant information. A slightly more concise version could exist.

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?

Given no output schema, the description thoroughly explains what the tool computes, covering all sections, wage floors/caps, and the phased schedule. It is comprehensive for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all 4 parameters. The tool description adds significant meaning beyond schema, such as explaining the ceiling phases for year, the flat contribution for Section 39, and the wage floor. This helps the agent understand parameter usage.

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 uses specific verbs ('Computes') and resources ('Thai Social Security Office (SSO) contribution') and clearly distinguishes from sibling tools by focusing on Thailand-specific contribution calculation with the 2026 ceiling unfreeze.

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 explains when to use the tool: for computing SSO contributions under the new ceiling, especially when old answers are wrong. It also outlines the three sections. It does not explicitly state when not to use, but the context is clear among siblings.

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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