Calculator MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct mathematical operation with no overlap in purpose. The descriptions are precise and unambiguous, making it easy for an agent to select the correct tool for addition, subtraction, multiplication, division, exponentiation, or square root calculation.
Naming Consistency5/5
All tool names follow a consistent verb-based pattern (add, divide, multiply, power, sqrt, subtract) that directly describes the mathematical operation. There are no deviations in naming conventions, and the pattern is predictable and readable throughout the set.
Tool Count5/5
With 6 tools, this server is well-scoped for a calculator domain, covering essential arithmetic operations (add, subtract, multiply, divide) and common advanced functions (power, sqrt). Each tool earns its place without redundancy, and the count is appropriate for the purpose.
Completeness5/5
The tool set provides complete coverage for a basic calculator domain, including all core arithmetic operations and key mathematical functions. There are no obvious gaps, such as missing operations like modulo or trigonometric functions, which are not essential for this scope, and agents can perform typical calculations without dead ends.
Average 3.2/5 across 6 of 6 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
- CI is passing
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. It states the operation but fails to mention critical behaviors such as handling division by zero (e.g., errors or exceptions), output format (e.g., integer vs. float), or edge cases like negative numbers. For a mutation tool (division changes values) 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 sentence ('Divide first number by second number'), which efficiently conveys the core action without unnecessary words. It is front-loaded and wastes no space, making it easy for an agent to parse quickly.
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 (simple arithmetic but with potential errors like division by zero), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects, error handling, or output details, which are crucial for an agent to use the tool correctly. The schema handles parameters well, but overall context 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 parameters 'a' (Dividend) and 'b' (Divisor). The description adds no additional meaning beyond what the schema provides, as it only repeats the division concept without explaining parameter roles or constraints. Baseline 3 is appropriate since the schema adequately documents parameters, but the description doesn't enhance understanding.
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 with a specific verb ('Divide') and identifies the resources involved ('first number by second number'). It distinguishes from siblings like 'add', 'multiply', and 'subtract' by specifying division. However, it doesn't explicitly mention the mathematical operation name beyond the verb, which slightly limits differentiation from other arithmetic tools.
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 like 'multiply' or 'subtract'. It lacks context about mathematical scenarios where division is appropriate, prerequisites (e.g., avoiding division by zero), or comparisons to sibling tools. This leaves the agent with minimal usage direction beyond the basic operation.
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. It states the basic operation but does not disclose behavioral traits such as error handling for negative inputs, performance characteristics, or output format. This leaves gaps in understanding how the tool behaves beyond the core 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 a single, efficient sentence with zero waste. It is front-loaded and directly conveys the tool's purpose without unnecessary details, making it easy for an AI agent to parse quickly.
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 does not address error cases, return values, or usage context, which are important for a mathematical operation tool. This reduces its effectiveness in guiding an AI agent fully.
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 input schema has 100% description coverage, with the parameter 'number' clearly documented. The description adds no additional meaning beyond the schema, such as constraints or examples. Since the schema does the heavy lifting, 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 'Calculate square root of a number' clearly states the verb ('calculate') and resource ('square root of a number'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'power' (which could also compute roots), so it falls short of 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. For example, it does not mention using 'power' for other root calculations or specify that this is for non-negative numbers (though implied by the operation). This lack of context reduces its utility for an AI agent.
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 states the action ('multiply') but doesn't describe any behavioral traits such as error handling (e.g., for non-numeric inputs), performance characteristics, or side effects. For a mathematical operation tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond the 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 a single, efficient sentence ('Multiply two numbers together') that directly states the tool's purpose with zero waste. It is appropriately sized for a simple tool and front-loaded, making it easy for an agent to parse quickly.
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?
Given the tool's low complexity (a basic arithmetic operation), 100% schema coverage, and no output schema, the description is minimally adequate. It states what the tool does but lacks details on usage guidelines, behavioral transparency, or output expectations. For such a simple tool, this might be sufficient, but it doesn't provide complete context for optimal agent 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?
The input schema has 100% description coverage, with clear documentation for both parameters ('a' and 'b'). The description adds no additional meaning beyond what the schema provides—it merely restates that two numbers are multiplied. According to the rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
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 as 'Multiply two numbers together', which is a specific verb (multiply) applied to resources (two numbers). It distinguishes from siblings like 'add' or 'divide' by specifying the multiplication operation. However, it doesn't explicitly differentiate from all siblings (e.g., 'power' could also involve multiplication), so it doesn't reach the highest 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 like 'add', 'divide', 'power', 'sqrt', or 'subtract'. It doesn't mention any context, prerequisites, or exclusions for usage. The agent must infer usage based on the tool name alone, which is insufficient for optimal selection.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the mathematical operation but doesn't mention error handling (e.g., large exponents, decimal exponents), performance characteristics, or any limitations. For a mathematical tool with zero annotation coverage, this is a significant gap in behavioral context.
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 a single, efficient sentence that directly states the tool's function with zero wasted words. It's appropriately sized for this simple mathematical operation and front-loads the essential information without unnecessary elaboration.
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?
For a simple mathematical operation with 100% schema coverage and no output schema, the description is adequate but has clear gaps. It explains what the tool does but doesn't provide usage guidance relative to siblings, behavioral context, or output expectations. The description meets minimum requirements but could be more helpful for an AI agent.
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 both parameters clearly documented in the schema. The description adds minimal value beyond the schema by specifying which parameter is the base and which is the exponent, but doesn't provide additional context about valid ranges, special cases, or mathematical properties beyond what's already in the structured schema.
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 mathematical operation (raise to power) and identifies the two input numbers (first number as base, second as exponent). It distinguishes from siblings like add, subtract, multiply, and divide by specifying exponentiation, but doesn't explicitly differentiate from sqrt which is a related but distinct operation.
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 like sqrt (which calculates square roots) or other mathematical operations. It simply states what the tool does without context about appropriate use cases or comparisons to sibling tools.
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. While 'Add two numbers together' clearly indicates a computational operation, it doesn't describe error handling, precision limitations, overflow behavior, or return format. For a mathematical tool with zero annotation coverage, this represents a significant gap in behavioral 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 consists of a single, perfectly efficient sentence that communicates the core functionality without any wasted words. It's appropriately sized for this simple mathematical operation and front-loads the essential information immediately.
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?
For a simple mathematical tool with 100% schema coverage and no output schema, the description adequately communicates the basic operation. However, it lacks information about return values, error conditions, or numerical limitations that would be helpful for an agent. The description meets minimum requirements but could provide more complete 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 both parameters 'a' and 'b' clearly documented as 'First number to add' and 'Second number to add' respectively. The description adds no additional parameter information beyond what the schema already provides, so the baseline score of 3 is appropriate when the schema does all the heavy lifting.
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 'Add two numbers together' clearly states the specific verb ('add') and resource ('two numbers'), making the purpose immediately obvious. It distinguishes this tool from sibling tools like subtract, multiply, divide, power, and sqrt by specifying the exact 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for addition of two numbers, but provides no explicit guidance on when to use this tool versus alternatives like subtract or multiply. There are no exclusions or prerequisites mentioned, leaving the agent to infer usage from the tool name and context 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 states the operation but lacks details on error handling (e.g., for non-numeric inputs), performance characteristics, or output format. This is a significant gap 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose with zero waste. It is appropriately sized and front-loaded, making it easy to understand at a glance.
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?
Given the tool's low complexity (simple arithmetic), no annotations, no output schema, and high schema coverage, the description is minimally adequate. It covers the basic operation but lacks context on output values, error conditions, or behavioral traits, which could be helpful for an AI agent.
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 the schema (e.g., 'a' as 'Number to subtract from', 'b' as 'Number to subtract'). The description adds no additional meaning beyond what the schema provides, such as examples or edge cases, so it meets the baseline for high schema coverage.
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 'Subtract second number from first number' clearly states the specific mathematical operation (subtract) and identifies the resources (numbers). It distinguishes from sibling tools like 'add', 'divide', 'multiply', 'power', and 'sqrt' by specifying the exact arithmetic operation.
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 implies usage for subtraction operations but provides no explicit guidance on when to use this tool versus alternatives like 'add' or 'divide'. No context about mathematical scenarios or exclusions is mentioned, leaving usage to inference from the tool name and description.
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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