Math Mcp Server
@111-test-111
关于 Math Mcp Server
This project implements a math MCP server with the help of fastmcp, aiming to provide tools for LLMs to perform complex mathematical calculations and analyses.
基本信息
配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"math-calculator": {
"command": "uvx",
"args": [
"math-mcp"
],
"env": {
"OUTPUT_PATH": "path/to/plot_output",
"FONT_PATH": "/path/to/font"
}
}
}
}工具
22Brief description: Basic arithmetic operations tool for standard mathematical operations. For factorial, use mathematical_functions tool instead Examples: basic_arithmetic(operation='add', numbers=[1, 2, 3, 4, 5]) basic_arithmetic(operation='multiply', numbers=[2.5, 3.7], precision=3) basic_arithmetic(operation='power', numbers=[2, 3]) # Calculate 2^3
Brief description: Mathematical function calculation tool, supporting trigonometric, logarithmic, exponential functions, etc. Examples: mathematical_functions(function='sin', value=1.57, angle_unit='radians') mathematical_functions(function='log', value=100, base=10)
Brief description: Number format conversion tool, supporting base conversion, scientific notation, etc. Examples: number_converter(number='255', from_base=10, to_base=16) number_converter(number='1010', from_base=2, to_base=10)
Brief description: Physical unit conversion tool, supporting length, weight, temperature, etc., unit conversions. Examples: unit_converter(value=100, from_unit='cm', to_unit='m', unit_type='length') unit_converter(value=32, from_unit='fahrenheit', to_unit='celsius', unit_type='temperature')
Brief description: High-precision calculation tool using decimal arithmetic for enhanced accuracy. Provides precise calculations where floating-point errors matter. // Examples: precision_calculator(numbers=[1.123, 2.987], operation='add', precision_digits=15) precision_calculator(numbers=[2], operation='sqrt', precision_digits=20) precision_calculator(numbers=[5], operation='factorial', precision_digits=10)
Brief description: Numerical property analysis tool, analyzes various mathematical properties of numbers. Examples: number_properties(number=17, analysis_type='comprehensive') number_properties(number=100, analysis_type='factor')
Brief description: Matrix and linear algebra calculation tool, supporting basic operations and advanced analysis. Examples: matrix_calculator(operation='multiply', matrix_a=[[1,2],[3,4]], matrix_b=[[5,6],[7,8]]) matrix_calculator(operation='eigenvalues', matrix_a=[[4,2],[1,3]])
Brief description: Comprehensive statistical analysis tool, supporting descriptive statistics, hypothesis testing, and distribution analysis. Examples: statistics_analyzer(data1=[1,2,3,4,5], analysis_type='descriptive') statistics_analyzer(data1=[1,2,3], data2=[4,5,6], analysis_type='comparison')
Brief description: Advanced calculus computation engine, supporting derivatives, integrals, limits, series, and differential equations. Examples: calculus_engine(expression='x**2 + 3*x + 1', operation='derivative', variable='x') calculus_engine(expression='sin(x)', operation='integral', variable='x', limits=[0, 3.14159])
Brief description: Professional optimization suite, supporting function optimization, constraint optimization, root finding, and linear programming. Examples: optimization_suite(objective_function='x**2 + y**2', variables=['x', 'y'], operation='minimize') optimization_suite(equation='x**2 - 4', operation='find_roots')
Brief description: Regression analysis and machine learning modeling tool, supporting various regression algorithms and prediction functions. Examples: regression_modeler(operation='fit', x_data=[[1], [2], [3]], y_data=[2, 4, 6], model_type='linear') regression_modeler(operation='predict', x_data=[[12]], training_x=[[1], [2], [3]], training_y=[2, 4, 6])
Brief description: Mathematical expression evaluation and symbolic computation tool. Examples: expression_evaluator(expression='2*x + 3*y', variables={'x': 5, 'y': 7}) expression_evaluator(expression='x**2 + 2*x + 1', mode='factor')
Brief description: Data visualization and chart creation tool, supporting various statistical chart types. Examples: create_and_save_chart(chart_type='line', x_data=[1,2,3,4], y_data=[1,4,2,3], title='Line Plot') create_and_save_chart(chart_type='histogram', data=[1,2,2,3,3,3,4,4,5], filename='histogram_plot')
Brief description: Mathematical function curve plotting tool, supporting function graph visualization and derivative analysis. Examples: plot_function_curve(function_expression='x**2 + 2*x + 1') plot_function_curve(function_expression='sin(x)', x_range=(-6.28, 6.28), filename='sine_wave')
Brief description: Powerful geometry calculation tool, supporting plane geometry, solid geometry, and analytical geometry calculations. Examples: geometry_calculator(shape_type='circle', operation='properties', dimensions={'radius': 5}) geometry_calculator(shape_type='triangle', operation='area', points=[[0,0], [3,0], [0,4]])
Brief description: Advanced number theory calculation tool, supporting prime testing, factorization, modular arithmetic, etc. Examples: number_theory_calculator(operation='prime_factorization', number=60) number_theory_calculator(operation='prime_test', number=97)
Brief description: Professional digital signal processing tool, supporting FFT, filtering, modulation/demodulation, etc. Examples: signal_processing_calculator(operation='generate_signal', signal_type='sine', frequency=10, sampling_rate=1000, duration=1) signal_processing_calculator(operation='fft', signal=[1,2,3,4,5,6,7,8], sampling_rate=8)
Brief description: Professional financial mathematics calculation tool, supporting compound interest, investment analysis, risk assessment, etc. Examples: financial_calculator(operation='compound_interest', principal=1000, rate=0.05, time=10) financial_calculator(operation='npv', cash_flows=[-1000, 300, 400, 500], rate=0.1)
Brief description: Probability and statistics calculation tool, supporting probability distributions, hypothesis testing, Bayesian analysis, etc. Examples: probability_calculator(operation='probability_mass', distribution='normal', parameters={'mu':0,'sigma':1}, x_value=1.96) probability_calculator(operation='cumulative_distribution', distribution='normal', parameters={'mu':20,'sigma':3}, x_value=25) probability_calculator(operation='random_sampling', distribution='binomial', parameters={'n':10,'p':0.3}, n_samples=100)
Brief description: Powerful complex analysis and complex function tool, supporting complex number form conversion, residue calculation, analytic continuation, complex plane visualization, and other advanced features. Examples: complex_analysis_suite(operation='convert_form', complex_number='3+4i') complex_analysis_suite(operation='function_evaluation', function_expression='z**2 + 1', complex_number='1+i') complex_analysis_suite(operation='residue_calculation', function_expression='1/(z**2 + 1)', singularities=['i', '-i'])
Brief description: Professional graph theory analysis tool, supporting shortest path, maximum flow, connectivity analysis, centrality calculation, community detection, spectral analysis, and other comprehensive graph theory functions. Examples: graph_theory_suite(operation='shortest_path', edge_list=[[1,2], [2,3], [1,3]], source_node=1, target_node=3) graph_theory_suite(operation='centrality_analysis', graph_data={'nodes': [1,2,3], 'edges': [[1,2], [2,3]]}) graph_theory_suite(operation='graph_visualization', adjacency_matrix=[[0,1,1],[1,0,1],[1,1,0]], filename='graph_plot')
Brief description: Deletes files generated in OUTPUT_PATH (or default temporary directory) and performs basic resource cleanup. Call only when the user explicitly indicates deletion of temporary or output files. Examples: cleanup_resources()
概览
What is Math Mcp Server?
Math Mcp Server is a Model Context Protocol (MCP) server that provides a collection of mathematical computation tools and plotting utilities, designed for integration with AI assistants or apps that support the MCP protocol.
How to use Math Mcp Server?
Run the server via uvx math-mcp. For use with Claude Desktop, add the configuration block shown in the README to your Claude Desktop config file, optionally setting OUTPUT_PATH and FONT_PATH environment variables.
Key features of Math Mcp Server
- Matrix computation: basic operations, decomposition, eigenvalues, SVD
- Statistical analysis: descriptive stats, hypothesis testing, distribution analysis
- Calculus: derivatives, integrals, limits, Taylor series
- Optimization algorithms: function optimization, linear programming, constrained optimization
- Regression analysis: linear, polynomial, and regularized regression
- Data visualization: statistical charts and function plotting
Use cases of Math Mcp Server
- Perform symbolic and numeric calculus tasks via an AI interface
- Conduct statistical hypothesis tests and descriptive analysis
- Solve optimization problems including linear and constrained optimization
- Execute matrix decompositions and eigenvalue computations
- Generate plots and charts from mathematical functions or data
FAQ from Math Mcp Server
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