Sri Lanka Payslip

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds context by listing the specific calculated outputs (APIT/PAYE, EPF, ETF, etc.) and confirming the estimation nature, consistent 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.

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

The description is a single, well-formed sentence of 25 words that front-loads the tool's purpose and lists key outputs without any extraneous content.

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

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

The description covers the scope (regular primary employment in Sri Lanka) and lists all major components. Given the existence of an output schema and comprehensive annotations, it is complete enough for a calculation tool.

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

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

Schema coverage is 100% with descriptive parameter comments. The description provides a high-level context but does not add any new meaning beyond what the schema already states for each parameter. Baseline 3 is appropriate.

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

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

The description specifies the verb 'Estimate' and the resource: monthly APIT/PAYE, EPF, ETF, stamp duty, net pay, and employer cost. It clearly distinguishes from sibling tools (reference and checklist) by focusing on computation of payslip components.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or compare with sibling tools.

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds value by specifying what data is returned (APIT, EPF, ETF, stamp-duty), that it is maintained, and that sources and scope limits are included—context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single, front-loaded sentence with no redundancy. Every word contributes meaningful information: the verb, the specific data categories, and the qualifying details about sources and scope.

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

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

With no parameters, an existing output schema, and simple read-only behavior, the description is sufficient. It covers the purpose, content, and key qualifiers (maintained, official sources, scope limits), leaving detailed return structure to the output schema.

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

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

The tool has zero parameters, so the baseline is 4. The description does not need to explain parameter meaning and appropriately remains silent on this front.

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

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

The description clearly states the tool's function with a specific verb ('Return') and resource ('maintained APIT, EPF, ETF, and stamp-duty assumptions'), and adds context about official sources and scope limits. This unambiguously distinguishes it from sibling tools like calculate_sri_lanka_payslip and get_sri_lanka_payslip_checklist.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage (when you need reference assumptions) but does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or sibling tools. It provides clear context but lacks explicit guidance on selection.

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

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds that it returns checklist fields, but does not disclose additional behavioral traits such as whether the checklist is static or based on input. Given the strong annotation coverage, a score of 3 is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single, front-loaded sentence with no filler words. It efficiently conveys the tool's purpose without redundancy.

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

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

With zero parameters, a rich annotation set, and an output schema, the description is adequate. It communicates the tool's scope (Sri Lankan payslip) and function (return checklist items). No additional context is needed.

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

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

The input schema has 0 parameters, so the baseline is 4. The description adds no parameter semantics, but none are needed since the tool requires no input.

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

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

The description uses the specific verb 'Return' and identifies the resource as 'fields and checks needed to prepare or review a Sri Lankan employee payslip.' This clearly distinguishes it from siblings like calculate_sri_lanka_payslip (calculation) and get_sri_lanka_payroll_reference (reference).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states the tool returns checklist items 'needed to prepare or review' a payslip, providing clear context for when to use it. It does not explicitly name alternatives or exclude other tools, but the intended use case is evident.

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