Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Math & Calculator MCP Server solve the quadratic equation 2x² + 5x - 3 = 0"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A powerful Model Context Protocol (MCP) Server that provides advanced mathematical utilities and calculator tools for AI assistants like Claude and other MCP-compatible clients.
This MCP server exposes mathematical tools that AI assistants can use to perform calculations, statistical analysis, unit conversions, and more. Built with the Model Context Protocol SDK, it seamlessly integrates with Claude Desktop, VSCode, and other MCP clients.
Author: Jefferson Rosas Chambilla
Repository: https://github.com/Ankluna72/Math-Calculator-MCP-Server-
Related MCP server: Calculator MCP Server
Addition, subtraction, multiplication, division
Power, square root, modulo operations
Error handling (division by zero, invalid operations)
Mean, median, mode
Standard deviation and variance
Complete statistical summaries
Length: meters, kilometers, miles, feet, inches
Weight: kilograms, grams, pounds, ounces
Temperature: Celsius, Fahrenheit, Kelvin
Quadratic equation solver (ax² + bx + c = 0)
Handles real and complex solutions
Discriminant analysis
Percentage of a number
Percentage increase/decrease
"What percentage is X of Y?"
Sin, cos, tan (and inverse functions)
Angle calculations in degrees
Precise floating-point results
Node.js >= 18.0.0
npm or yarn
git clone https://github.com/Ankluna72/Math-Calculator-MCP-Server-.git
cd Math-Calculator-MCP-Server-
npm install
npm run buildAdd to your Claude Desktop config file (claude_desktop_config.json):
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
{
"mcpServers": {
"math-calculator": {
"command": "node",
"args": [
"C:\\path\\to\\Math-Calculator-MCP-Server-\\dist\\index.js"
]
}
}
}Add to VSCode settings (.vscode/settings.json or User Settings):
{
"mcp.servers": {
"math-calculator": {
"command": "node",
"args": [
"C:\\path\\to\\Math-Calculator-MCP-Server-\\dist\\index.js"
]
}
}
}Once configured, your AI assistant can use these tools automatically. Here are some example requests:
"Calculate 25 * 4"
"What is the square root of 144?"
"Divide 100 by 7""Calculate the mean of [10, 20, 30, 40, 50]"
"Find median and mode for these numbers: [5, 3, 5, 2, 8, 5]"
"Give me all statistics for [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]""Convert 100 kilometers to miles"
"How many pounds is 75 kilograms?"
"Convert 32 Fahrenheit to Celsius""Solve x² - 5x + 6 = 0"
"Find solutions for 2x² + 3x - 2 = 0""What is 15% of 200?"
"Increase 50 by 20%"
"What percentage is 25 of 200?""Calculate sin(30°)"
"What is cos(45°)?"
"Find tan(60°)"Tool | Description |
| Basic arithmetic operations |
| Statistical analysis of number arrays |
| Convert between different units |
| Solve quadratic equations |
| Percentage calculations |
| Trigonometric functions |
Math-Calculator-MCP-Server-/
├── src/
│ └── index.ts # Main MCP server implementation
├── dist/ # Compiled JavaScript (after build)
├── package.json # Project dependencies
├── tsconfig.json # TypeScript configuration
├── .gitignore
└── README.md# Install dependencies
npm install
# Build the project
npm run build
# Development mode (watch for changes)
npm run dev
# Start the server
npm startYou can test the server manually using stdio communication:
npm startThen send MCP protocol messages via stdin to test tool execution.
MIT License - see LICENSE file for details
Contributions are welcome! Please feel free to submit a Pull Request.
Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request
Jefferson Rosas Chambilla
GitHub: @Ankluna72
Repository: Math-Calculator-MCP-Server-
Built with the Model Context Protocol SDK
Inspired by the need for mathematical tools in AI assistants
Thanks to Anthropic for developing the MCP standard
⭐ If you find this project useful, please consider giving it a star on GitHub!
Perform basic arithmetic operations (add, subtract, multiply, divide, power, sqrt, modulo)
| Name | Required | Description | Default |
|---|---|---|---|
| operation | Yes | The mathematical operation to perform | |
| a | Yes | First number | |
| b | No | Second number (not required for sqrt) |
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 it lists operations, it doesn't mention error handling (e.g., division by zero, sqrt of negative numbers), precision limits, input constraints, or what the output format looks like. For a tool with multiple operations and no annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that lists all operations without unnecessary words. It's appropriately sized and front-loaded with the core purpose, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple operations with different parameter requirements) and lack of both annotations and output schema, the description is insufficient. It doesn't cover behavioral aspects like error cases, output format, or usage distinctions from siblings, leaving the agent with incomplete context for reliable tool invocation.
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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by listing the operations that map to the 'operation' enum, but doesn't provide additional context about parameter usage (e.g., that 'b' is optional for 'sqrt', or how 'power' interprets 'a' and 'b'). Baseline 3 is appropriate when the schema does the heavy lifting.
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 purpose: 'Perform basic arithmetic operations' followed by a specific list of operations (add, subtract, multiply, divide, power, sqrt, modulo). It uses a specific verb ('perform') and identifies the resource ('arithmetic operations'), but doesn't explicitly differentiate from sibling tools like 'solve_equation' or 'trigonometry' which might also involve calculations.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'solve_equation' for algebraic problems, 'percentage' for percentage calculations, or 'trigonometry' for trigonometric functions, leaving the agent to infer usage context from the operation list alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert between different units (length, weight, temperature)
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | Value to convert | |
| from | Yes | Source unit (e.g., 'meters', 'kilometers', 'celsius', 'fahrenheit', 'kg', 'pounds') | |
| to | Yes | Target unit |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states what the tool does but doesn't disclose behavioral traits like whether it handles edge cases (e.g., invalid units), precision of conversions, error handling, or rate limits. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 extremely concise and front-loaded with a single sentence that directly states the tool's purpose and scope. Every word earns its place with no redundant information, making it easy to parse quickly.
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?
Given the tool's moderate complexity (unit conversion across categories) and 100% schema coverage but no annotations or output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, error handling, and output format, which are important for a conversion tool with multiple parameter types.
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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema by listing example unit categories, but doesn't provide additional semantics like unit compatibility rules or conversion formulas. Baseline 3 is appropriate when the schema does the heavy lifting.
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 purpose: 'Convert between different units' with specific categories listed (length, weight, temperature). It distinguishes from siblings like 'calculate' or 'solve_equation' by focusing on unit conversion rather than general computation. However, it doesn't explicitly differentiate from all siblings (e.g., 'percentage' could involve unit-like conversions).
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when unit conversion is needed versus using 'calculate' for mathematical operations, 'percentage' for percentage calculations, or other siblings. There are no explicit when/when-not statements or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculate percentages (percentage of a number, percentage increase/decrease, what percentage X is of Y)
| Name | Required | Description | Default |
|---|---|---|---|
| operation | Yes | Type of percentage calculation | |
| value1 | Yes | First value | |
| value2 | Yes | Second value |
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 what calculations are performed but does not describe behavioral traits such as error handling (e.g., division by zero), input validation, output format, or performance characteristics. The description is functional but lacks transparency about how the tool behaves beyond its basic purpose.
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, efficient sentence that front-loads the core purpose ('Calculate percentages') and then specifies the exact operations without any wasted words. It is appropriately sized for a straightforward mathematical tool and every phrase contributes to understanding its function.
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?
Given the tool's low complexity (basic calculations), high schema coverage, and no output schema, the description is adequate but has gaps. It covers the purpose and operations but lacks details on behavioral aspects like error handling or output format, which are important for an agent to use it correctly without annotations. It is minimally viable but could be more complete.
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 100% description coverage, with clear parameter names and an enum for 'operation' that aligns with the calculation types in the description. The description adds minimal value beyond the schema by listing the operations, but it does not provide additional context like examples, formula details, or parameter relationships. Baseline 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.
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 specific verbs ('calculate percentages') and enumerates the exact types of calculations it performs (percentage of a number, percentage increase/decrease, what percentage X is of Y). It distinguishes itself from sibling tools like 'calculate' (general), 'convert_units', 'solve_equation', 'statistics', and 'trigonometry' by focusing exclusively on percentage operations.
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 by listing the calculation types, but it does not explicitly state when to use this tool versus alternatives like the general 'calculate' tool or other mathematical siblings. There is no guidance on prerequisites, exclusions, or specific contexts where this tool is preferred over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Solve quadratic equations (ax² + bx + c = 0)
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | Coefficient of x² | |
| b | Yes | Coefficient of x | |
| c | Yes | Constant term |
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 what the tool does (solving equations) but lacks details on traits like error handling (e.g., for invalid inputs or complex solutions), performance (e.g., speed or limitations), or output format. This leaves gaps for an AI agent to understand operational behavior.
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 extremely concise and front-loaded, consisting of a single, direct sentence: 'Solve quadratic equations (ax² + bx + c = 0)'. It wastes no words and immediately conveys the core functionality, making it efficient and easy to parse.
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?
Given the tool's moderate complexity (solving equations with 3 parameters) and no annotations or output schema, the description is minimally adequate. It covers the purpose but lacks behavioral details and output information. For a mathematical tool, it should ideally mention result types (e.g., real/complex roots) or error cases, but it meets a basic threshold.
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 100% description coverage, with clear parameter meanings (coefficients a, b, c). The description adds no additional semantic information beyond what the schema provides, such as constraints (e.g., a ≠ 0) or examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to heavily.
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 purpose: 'Solve quadratic equations (ax² + bx + c = 0)'. It specifies the verb ('solve') and resource ('quadratic equations'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'calculate' or 'trigonometry', which might also handle mathematical operations.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'calculate' or 'statistics', nor does it specify contexts or exclusions (e.g., for non-quadratic equations). Usage is implied by the equation form, but explicit alternatives or prerequisites are missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculate statistical measures (mean, median, mode, standard deviation, variance) from a list of numbers
| Name | Required | Description | Default |
|---|---|---|---|
| numbers | Yes | Array of numbers to analyze | |
| operation | Yes | Statistical operation to perform |
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 mentions what the tool does but lacks details on traits like error handling (e.g., for empty arrays or invalid operations), performance (e.g., computational limits), or output format (since no output schema exists). This is a significant gap for a tool with no structured safety or behavioral hints.
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, efficient sentence that front-loads the purpose and lists key operations without unnecessary words. Every part earns its place by specifying the action, measures, and input source, making it easy to scan and understand quickly.
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?
Given the tool's complexity (statistical operations with two parameters) and lack of annotations and output schema, the description is incomplete. It does not explain return values, error conditions, or behavioral constraints, which are crucial for an AI agent to use the tool correctly. More context is needed to compensate for missing structured data.
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 description coverage is 100%, so the schema already documents both parameters ('numbers' as an array and 'operation' with enum values). The description adds minimal value beyond the schema by implying the tool uses the numbers for statistical analysis, but it does not provide additional syntax, format details, or examples. Baseline 3 is appropriate when the schema handles most documentation.
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 specific action ('calculate statistical measures') and resources ('from a list of numbers'), listing the exact operations (mean, median, mode, standard deviation, variance). It distinguishes from sibling tools like 'calculate' (generic), 'convert_units', 'percentage', etc., by specifying statistical analysis rather than general calculations or other mathematical functions.
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 provides no guidance on when to use this tool versus alternatives like 'calculate' or other mathematical tools. It does not mention prerequisites (e.g., needing numeric data), exclusions (e.g., non-numeric inputs), or specific contexts where statistical analysis is preferred over other calculations, leaving usage decisions ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculate trigonometric functions (sin, cos, tan, asin, acos, atan) in degrees
| Name | Required | Description | Default |
|---|---|---|---|
| function | Yes | Trigonometric function | |
| angle | Yes | Angle in degrees |
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 tool calculates functions in degrees but doesn't mention error handling (e.g., for invalid inputs like tan(90)), performance characteristics, or output format. This leaves significant gaps for a computational tool.
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, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by specifying functions and units clearly.
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?
Given the tool's low complexity (2 parameters, no nested objects) and 100% schema coverage, the description is minimally adequate. However, with no output schema and no annotations, it should ideally explain the return value (e.g., a numeric result) and any behavioral constraints, which it doesn't do.
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 description coverage is 100%, so the schema already documents both parameters ('function' with enum values and 'angle' as a number in degrees). The description adds minimal value by specifying 'in degrees' for the angle, but this is implied in the schema. Baseline 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool calculates trigonometric functions (sin, cos, tan, asin, acos, atan) in degrees, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'calculate' or 'solve_equation', which might also handle mathematical operations.
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 provides no guidance on when to use this tool versus alternatives like 'calculate' or 'solve_equation'. It mentions the functions and units but doesn't specify contexts, exclusions, or prerequisites for choosing this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0calculateconvert_unitspercentagesolve_equationstatisticstrigonometryEach tool has a clearly distinct mathematical purpose with no overlap: calculate handles basic arithmetic, convert_units deals with unit conversions, percentage focuses on percentage calculations, solve_equation solves quadratic equations, statistics computes statistical measures, and trigonometry handles trigonometric functions. The descriptions are specific enough to prevent misselection.
All tool names follow a consistent verb_noun pattern (e.g., calculate, convert_units, solve_equation) using snake_case throughout. The naming is predictable and readable, with no deviations in style or convention.
With 6 tools, the server is well-scoped for a math and calculator domain, covering arithmetic, conversions, percentages, equations, statistics, and trigonometry. Each tool earns its place without being too sparse or bloated, fitting typical expectations for such a utility.
The tool set covers core mathematical operations comprehensively, including basic calculations, unit conversions, percentages, equation solving, statistics, and trigonometry. Minor gaps exist, such as no tools for calculus (e.g., derivatives/integrals) or geometry (e.g., area/volume calculations), but agents can work around these for most common math tasks.
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