Sri Lanka Payslip
Server Quality Checklist
Latest release: v2026.27.2
- Disambiguation5/5
Each tool has a clearly distinct purpose: calculation, reference data, and checklist. No overlap or ambiguity exists.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern (calculate_..., get_...) with clear, descriptive nouns. No mixed conventions.
Tool Count4/5Three tools is a reasonable scope for a niche payslip server, covering calculation, reference, and checklist. It is not overly thin or excessive.
Completeness4/5The server covers the core needs: calculation, assumptions, and checklist. Minor gaps like an update endpoint for assumptions are not critical for a calculation-focused tool.
Average 4.3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 22 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
Tools from this server were used 12 times in the last 30 days.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it as readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds valuable behavioral context by using 'Estimate' to signal approximate results and 'regular primary employment' to limit scope. There is 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, information-dense sentence that front-loads the action and lists all key outputs. There is zero fluff—every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a full input schema, output schema, and safe annotations, the description only needs to convey purpose and scope, which it does well. It lacks explicit mention of assumptions (e.g., tax year, latest rates) but those are likely captured in the output schema, making it adequately complete for this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema descriptions cover 100% of the 3 parameters (grossSalary, epfEtfEarningsBase, otherEmployeeDeductions), so the description does not need to repeat them. The description adds no extra parameter-level detail, keeping the score at the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Estimate' (specific verb) and names the exact resource: monthly APIT/PAYE, EPF, ETF, stamp duty, net pay, and employer cost. This clearly differentiates it from the sibling tools (get_sri_lanka_payroll_reference and get_sri_lanka_payslip_checklist) by focusing on calculation rather than reference or checklist.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by specifying 'regular primary employment in Sri Lanka', implying it is for standard single-job monthly payroll. However, it does not explicitly state when not to use it or mention alternatives, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
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.
Conciseness5/5Is 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.
Completeness5/5Given 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.
Parameters4/5Does 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.
Purpose5/5Does 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.
Usage Guidelines3/5Does 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.
- Behavior3/5
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.
Conciseness5/5Is 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.
Completeness5/5Given 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.
Parameters4/5Does 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.
Purpose5/5Does 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.
Usage Guidelines4/5Does 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.
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