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Get Shif Contribution

get_shif_contribution
Read-onlyIdempotent

Calculate monthly SHIF contribution from a gross monthly salary in KES, including KES 300 minimum and employee-only payment. Use to answer what SHIF deducts from pay.

Instructions

Calculate the monthly Social Health Insurance Fund (SHIF) contribution for a gross monthly salary in KES. Use when someone asks what SHIF takes from their pay. SHIF replaced NHIF in October 2024: 2.75% of gross pay, minimum KES 300, no cap, paid by the employee only. Returns gross_salary_kes, employee_contribution_kes, employer_match_kes (0), total_monthly_kes, the rate, the source (Social Health Insurance Act 2023; sha.go.ke premium rates, checked 2026-10-05) and a note to verify current rates. Read-only.

Input Schema

NameRequiredDescriptionDefault
gross_salary_kesYesGross monthly salary in KES.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.10

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already mark this read-only, idempotent and closed-world, and the description goes well beyond them: it discloses the exact rate (2.75%), the KES 300 floor, the absence of a cap, that the employee pays alone with zero employer match, the legal source with a checked-on date, and a caveat to verify current rates. That is unusually rich behavioral context for a 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose, trigger, then the rules and return shape, so an agent gets the essentials in the first two sentences. It runs long and the enumerated return fields are partially redundant with the output schema, and 'Read-only' repeats the annotation.

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 single-parameter calculator with an output schema and annotations already covering safety, every piece an agent needs is present: the trigger, the formula and floor, the payer, the provenance, and a freshness caveat. Nothing material is missing.

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 already documents 'Gross monthly salary in KES.' The description restates the same meaning ('for a gross monthly salary in KES') without adding format, bounds, or edge-case handling, so the baseline of 3 applies.

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?

States a specific verb (Calculate) and resource (monthly SHIF contribution) with the input unit (gross monthly salary in KES). The mention that 'SHIF replaced NHIF in October 2024' implicitly separates it from the sibling get_nhif_contribution, so an agent can route between them.

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?

'Use when someone asks what SHIF takes from their pay' gives a clear triggering context, which is stronger than most tools. However, it never names get_nhif_contribution as the alternative for pre-October-2024 periods, so the when-not/alternative routing is left to inference.

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