calcuris-mcp

by tresor4k

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool targets a distinct combination of country and tax type (e.g., us_income_tax vs au_stamp_duty), with no overlap in functionality. Descriptions clearly differentiate them.
Naming Consistency5/5
All tools follow a consistent pattern: two-letter country code (lowercase) followed by underscore and the specific tax type (e.g., us_income_tax, au_stamp_duty). This makes it easy to predict tool names.
Tool Count5/5
With 7 tools covering major tax calculations for multiple countries, the count feels well-scoped and manageable. Each tool serves a clear purpose without bloat.
Completeness3/5
The set covers several tax types but has notable gaps: missing UK income tax, Canadian property tax, and Australian property tax (only stamp duty is present). Agents may need additional tools for complete tax workflows.
Average 4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on July 3, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry the burden. It discloses the estimate nature and source of rates, but lacks details on limitations (e.g., dependents are informational) and output behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise with an example, but includes irrelevant source references (e.g., HMRC) that detract from focus. It could be more streamlined.
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, so the description should explain return values. It mentions what the tool computes but not the structure or format of the output, leaving the agent uncertain about the response.
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%, so baseline is 3. The description adds minimal extra meaning beyond what the schema already provides, only including an example and noting stateCode is optional.
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?
Description clearly states it computes US take-home pay per pay period, listing specific components (federal withholding, Social Security, etc.) and distinguishes from sibling tools like us_income_tax and us_property_tax.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides an example and mentions it is an estimate for guidance, but does not explicitly state when to use this tool versus alternatives or when not to use it.
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?
The description notes it is an estimate for guidance based on 2026 rates, but does not disclose limitations such as what is not included (e.g., AMT, investment income) beyond the Child Tax Credit. With no annotations, the description carries the full burden, and more detail on behavioral constraints would improve transparency.
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 two sentences plus an example, front-loading the main function. However, the parenthetical list of sources ('IRS/Tax Foundation/...') is verbose and could be shortened or moved to a note, slightly reducing conciseness.
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?
The description does not specify the return value structure (e.g., whether it returns federal tax, state tax, effective rate). With no output schema, more detail on the output would improve completeness. The given description is adequate but leaves gaps for a 7-parameter tool.
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 descriptions for all parameters, so baseline is 3. The description adds value by including an example and clarifying that stateCode is optional, and by noting the tax year (2026, post-OBBBA) which adds context beyond the schema.
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 US federal income tax plus estimated state income tax with a progressive bracket breakdown, using specific verb and resource. It distinguishes from siblings like 'ca_income_tax' and 'us_paycheck' by specifying federal and multi-state scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes an example but does not explicitly state when to use this tool versus alternatives like 'ca_income_tax' or 'us_paycheck'. Usage context is implied through the example but lacks clear when-not-to-use guidance or differentiation from sibling tools.
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?
With no annotations, the description carries full burden. It mentions 'estimate for guidance' and cites official sources, hinting at non-binding output. However, it does not explicitly state that the tool is read-only, has no side effects, or what its limitations are (e.g., only 2025-26 rates, not all deductions).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first lists functionality and gives an example, the second adds source and disclaimer. It is front-loaded with the most important information and contains no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, and the description fails to describe the return format or contents. It says 'computes' these components but does not specify whether the output is a single number, a breakdown, or an object. This is a significant gap for a calculation tool.
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 descriptions, but the tool description adds value by explaining how parameters affect the calculation (e.g., privateHospitalCover avoids MLS, hasHelpDebt triggers repayment) and listing the components. This goes beyond the schema's basic parameter definitions.
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 Australian resident income tax with specific components (Medicare levy, surcharge, LITO, HELP/HECS) and provides a concrete example. It distinguishes from sibling tools like us_income_tax and ca_income_tax by specifying the tax jurisdiction and year.
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 implies usage for Australian residents and provides an example, but does not explicitly state when not to use (e.g., for non-residents or other tax calculations). Sibling tools are different countries, so context is clear but exclusions are absent.
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?
No annotations exist, so the description must disclose behavior. It states the calculation is an 'estimate for guidance' using '2026 rates from official sources', which adds some transparency. However, it does not explain limitations, accuracy bounds, or how partial tax credits (e.g., for deductions) are handled.
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 two sentences long with a code example, making it efficient. It front-loads the core purpose and includes a typical usage example, though it could be slightly more structured (e.g., bullet points for parameters).
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?
With 4 parameters, no output schema, and a complex calculation involving multiple jurisdictions, the description gives a high-level overview but lacks details on the output structure (e.g., breakdown of tax, CPP, EI) and edge cases (e.g., zero income). It is adequate but not fully comprehensive.
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%, so baseline is 3. The description adds value by explaining grossIncome as 'annual employment income in CAD', provinceCode as a two-letter code with valid codes listed, rrspContribution as deducted from taxable income, and compareAllProvinces behavior. This goes beyond the schema's property descriptions.
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 tool name, title, and description clearly indicate it computes Canadian federal and provincial income tax for 2026, including CPP/EI and take-home pay. The example and sibling tools (e.g., us_income_tax) confirm its geographic specificity, making purpose unmistakable.
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 an example and mentions the compareAllProvinces option, giving usage context. However, it does not explicitly state when to use this tool versus alternatives (though sibling tools are country-specific) or mention prerequisites like 'grossIncome' must be in CAD.
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?
No annotations are provided, so the description carries the full burden. It notes the estimate is for guidance and cites official sources, but doesn't disclose limitations like local variations or updates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences and an example, no wasted words, and front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters and no output schema, the description provides an example and source note, but could mention return format (annual/monthly) and what values are estimated.
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 coverage is 100%, so baseline is 3. The description includes an example but adds no extra parameter meaning beyond the schema descriptions.
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 specifies the tool estimates US property tax based on home value and either a custom rate or a state's effective rate. It clearly distinguishes from sibling tools like us_income_tax by focusing on property tax.
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 implies when to use (property tax estimation) but doesn't explicitly state when not to use or list alternatives. However, siblings cover different tax types, making the context clear.
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?
No annotations provided, so description bears full burden. It discloses the calculation method, allowances, and estimation nature. Does not mention any destructive behavior or permission needs, but the tool appears read-only and safe.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences plus an example that effectively demonstrate usage. No redundant or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers the method, allowances, and example. Lacks explicit description of the output format (e.g., breakdown of tax components) but mentions 'year-over-year comparison', implying the result structure. Adequate for a complex tax calculator.
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%, so parameters are already well-documented. The description adds an example and reinforces the meaning of parameters (e.g., otherIncome excludes dividends), but does not significantly extend beyond the schema.
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?
Clearly states the tool computes UK dividend tax using HMRC's stacking method, with specific details about allowances and year comparison. Distinguishes from sibling tools focused on other countries or tax types.
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?
Provides a clear example and context for using the tool, but does not explicitly state when not to use or mention alternatives. However, sibling tools cover different jurisdictions and tax types, making the intended usage fairly obvious.
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?
No annotations provided, so description carries the burden. Discloses use of 2026 rates from official sources, estimates nature, and coverage of concessions. Lacks details on rate limits or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus an example; front-loaded with purpose. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all parameters and gives usage context. However, missing return value details and error cases; no output schema to compensate.
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%, but description adds value with an example, explanation of 'compareAllStates', and clarification on boolean parameters. Provides context beyond schema.
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?
Clear verb 'Computes' with specific resource 'Australian state/territory stamp duty' and details on concessions and surcharges. Distinct from sibling tools which handle other tax types.
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?
Provides an example and explains the 'compareAllStates' option. However, it does not explicitly state when to use this tool versus alternatives, though sibling context implies differentiation.
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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