Calculator MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct mathematical purpose: basic arithmetic, factorial, logarithms, square roots, and trigonometry. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the operation needed.
Naming Consistency4/5
Most tools follow a consistent naming pattern with descriptive, single-word names (calculate, factorial, logarithm, sqrt, trigonometry). However, 'sqrt' is an abbreviation while others are full words, causing a minor deviation from perfect consistency.
Tool Count5/5
With 5 tools, this server is well-scoped for a calculator domain. Each tool serves a distinct mathematical function, and the count is neither too sparse nor overwhelming, fitting typical calculator operations efficiently.
Completeness4/5
The toolset covers core mathematical operations (arithmetic, factorial, logarithms, square roots, trigonometry), but there are minor gaps such as missing advanced functions like hyperbolic trigonometry or statistical calculations. However, agents can handle most common calculator tasks without significant issues.
Average 2.7/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT License.
This repository includes a README.md file.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. '对数计算' only states the operation without explaining behavioral traits such as error handling (e.g., for negative numbers or base=1), computational limits, or output format. This is inadequate for a tool with no annotation coverage.
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 a single phrase '对数计算', which is extremely concise and front-loaded. However, it's under-specified rather than efficiently informative, as it lacks necessary details. It earns a 4 for brevity but loses points for not using its minimal space effectively.
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?
Given no annotations, no output schema, and a simple mathematical tool with 2 parameters, the description is incomplete. It doesn't explain the return value (e.g., a numeric result), error conditions, or how it differs from sibling tools. This leaves significant gaps for an agent to use it correctly.
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%, with clear parameter descriptions in Chinese ('真数' for number, '底数（可选，默认为 e）' for base with default e). The description adds no meaning beyond the schema, but the schema does the heavy lifting, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description '对数计算' (logarithm calculation) restates the tool name 'logarithm' in Chinese, making it a tautology. It doesn't specify what kind of logarithm calculation (e.g., natural log, base-10, custom base) or distinguish it from sibling tools like 'calculate' or 'factorial'. The purpose is vague beyond the obvious mathematical operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'calculate' (which might handle general math) or other mathematical siblings. The description offers no context, exclusions, or prerequisites, leaving the agent with no usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the basic operation without disclosing behavioral traits such as error handling (e.g., for negative inputs), performance characteristics, or output format. For a mathematical tool with no annotation coverage, this is a significant gap in 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 a single phrase '计算平方根', which is appropriately sized and front-loaded. It wastes no words, but could be slightly more informative. It earns its place by stating the core function efficiently, though it lacks additional context that might be useful.
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?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description is incomplete. It doesn't explain the return value (e.g., numeric result or error), handle edge cases, or provide context for use among siblings. For a basic mathematical function, more detail on behavior and output would improve completeness.
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?
The schema description coverage is 100%, with the parameter 'number' documented as '要计算平方根的数字' (the number to calculate the square root of). The description doesn't add meaning beyond what the schema provides, such as constraints or examples. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description '计算平方根' (calculates square root) states the basic function but is vague about scope and differentiation. It doesn't specify what type of numbers it handles (e.g., only non-negative) or how it distinguishes from sibling tools like 'calculate' or 'logarithm'. The purpose is clear but lacks specificity and sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., input must be non-negative), exclusions, or comparisons to sibling tools like 'calculate' (which might handle general operations) or 'factorial'. Usage is implied from the name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the operation without mentioning potential issues like handling negative numbers, large inputs causing overflow, or performance characteristics. For a mathematical tool with zero annotation coverage, this is a significant gap in transparency.
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 extremely concise with a single phrase '计算阶乘', which is front-loaded and wastes no words. It directly states the tool's function without unnecessary elaboration, making it efficient for quick understanding.
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?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error handling, or behavioral traits, which are crucial for a mathematical function. The description alone is inadequate for an agent to use the tool effectively without additional context.
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%, with the parameter 'number' documented as '要计算阶乘的整数' (integer to calculate factorial). The description doesn't add any meaning beyond what the schema provides, such as valid ranges or edge cases. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description '计算阶乘' (calculates factorial) states the mathematical operation but is vague about scope and context. It doesn't specify what type of factorial (e.g., integer, for non-negative numbers) or distinguish it from sibling tools like 'calculate' which might perform similar operations. The purpose is clear at a basic level but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'calculate' or other mathematical functions. The description doesn't mention prerequisites, constraints (e.g., input range), or typical use cases, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what operations are performed without mentioning error handling (e.g., division by zero), input constraints (e.g., numeric types), output format, or performance characteristics. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 extremely concise and front-loaded: a single sentence in Chinese that directly states the tool's function. There's no wasted verbiage or unnecessary elaboration, making it efficient for quick understanding.
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?
Given the tool's complexity (basic math operations with 3 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like error handling, input validation, or result formatting. While the schema covers parameters well, the overall context for safe and effective use is insufficient.
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?
The schema description coverage is 100%, with clear descriptions for all parameters: 'operation' (运算类型), 'a' (第一个数字), and 'b' (第二个数字). The description adds minimal value beyond the schema by listing the operation types, but it doesn't explain parameter interactions or provide examples. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '执行基本数学运算（加、减、乘、除、幂、取模）' which translates to 'Perform basic mathematical operations (addition, subtraction, multiplication, division, power, modulo).' This specifies the verb ('perform') and resource ('basic mathematical operations') with a list of specific operations. However, it doesn't explicitly differentiate from sibling tools like factorial or logarithm, which handle more specialized mathematical functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like factorial, logarithm, sqrt, or trigonometry, nor does it specify contexts where basic operations are preferred over more specialized ones. There's no information about prerequisites, limitations, or common use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does (trigonometric calculations) without any information about output format, error handling, precision, domain restrictions (e.g., tan at 90 degrees), or performance characteristics. This leaves significant gaps in understanding how the tool behaves beyond its basic function.
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 extremely concise—a single phrase in Chinese that directly states the tool's function. It is front-loaded with no unnecessary words, making it efficient and easy to parse. Every part of the description earns its place by specifying the mathematical operations.
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?
Given the tool's complexity (mathematical computation with potential edge cases) and the lack of annotations and output schema, the description is incomplete. It doesn't cover return values, error conditions, or behavioral nuances, which are critical for an AI agent to use the tool correctly. The description alone is insufficient for safe and effective invocation.
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%, with clear descriptions for both parameters ('function' as trigonometric type and 'angle' as degrees). The description adds no additional parameter semantics beyond what the schema provides, such as unit conventions or valid angle ranges. However, since the schema fully documents the parameters, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description '三角函数计算（sin、cos、tan）' clearly states the tool's purpose: it performs trigonometric calculations (sin, cos, tan). It specifies the exact mathematical functions supported, which distinguishes it from sibling tools like 'calculate' (generic), 'factorial', 'logarithm', and 'sqrt'. However, it doesn't explicitly mention the verb 'calculate' or specify the resource (angles), keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where trigonometric functions are preferred over other mathematical operations. The agent must infer usage based on the function names alone, which is insufficient for clear decision-making.
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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