{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_apetta-vibe-math-mcp","slug":"apetta-vibe-math-mcp","name":"Vibe Math","type":"mcp","url":"https://github.com/apetta/vibe-math-mcp","page_url":"https://unfragile.ai/apetta-vibe-math-mcp","categories":["mcp-servers","app-builders"],"tags":["mcp","model-context-protocol","smithery:apetta/vibe-math-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_apetta-vibe-math-mcp__cap_0","uri":"capability://data.processing.analysis.symbolic.and.numerical.calculus.evaluation","name":"symbolic-and-numerical-calculus-evaluation","description":"Evaluates derivatives and integrals using SymPy's symbolic engine with fallback to numerical methods (SciPy). Supports both symbolic differentiation/integration for closed-form solutions and numerical approximation for complex functions. Automatically selects the optimal method based on expression complexity and user intent, returning step-by-step symbolic derivations or precise numerical results with configurable precision.","intents":["I need to find the derivative of a complex function symbolically to understand its mathematical structure","Calculate a definite integral numerically when symbolic integration fails or is intractable","Get both the symbolic form and numerical evaluation of a limit or series expansion","Verify calculus homework by showing intermediate steps in symbolic manipulation"],"best_for":["mathematicians and engineers needing symbolic verification","students learning calculus with step-by-step explanations","researchers computing derivatives for optimization problems"],"limitations":["Symbolic integration may timeout on highly complex expressions requiring advanced techniques","Numerical integration accuracy depends on function smoothness and singularity handling","No support for multivariable calculus beyond single-variable derivatives and integrals","Series expansions limited to Taylor/Laurent series around specified points"],"requires":["SymPy 1.12+ for symbolic computation","SciPy 1.10+ for numerical integration (quad, dblquad)","Python 3.9+"],"input_types":["mathematical expression as string (e.g., 'x**2 + 3*x + 2')","variable specification (e.g., 'x', 'y')","integration bounds as floats or symbolic expressions"],"output_types":["symbolic expression (SymPy object serialized to string)","numerical result with precision metadata","step-by-step derivation trace","error bounds for numerical approximations"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_1","uri":"capability://data.processing.analysis.matrix.operations.with.decomposition","name":"matrix-operations-with-decomposition","description":"Performs matrix algebra operations (multiply, inverse, transpose, determinant, trace) and advanced decompositions (eigenvalue, SVD, QR, Cholesky, LU) using NumPy's BLAS/LAPACK bindings for computational efficiency. Handles both dense and sparse matrices, with automatic numerical stability checks and condition number reporting for ill-conditioned systems. Decompositions return both the factorized components and reconstruction verification.","intents":["Multiply large matrices efficiently and verify the result structure","Solve linear systems Ax=b using the most numerically stable decomposition","Compute eigenvalues and eigenvectors for principal component analysis or stability analysis","Decompose a matrix into QR or SVD form for least-squares problems or dimensionality reduction","Check if a matrix is invertible and compute its inverse with condition number reporting"],"best_for":["data scientists performing PCA or dimensionality reduction","engineers solving systems of linear equations","researchers analyzing matrix properties for numerical stability","ML practitioners needing matrix factorization for feature extraction"],"limitations":["Dense matrix operations scale as O(n³) for n×n matrices; very large matrices (>10k×10k) require sparse matrix handling","Eigenvalue computation for non-symmetric matrices may return complex eigenvalues; numerical precision depends on matrix conditioning","SVD on extremely ill-conditioned matrices may lose precision in smaller singular values","No built-in support for generalized eigenvalue problems (Ax = λBx)"],"requires":["NumPy 1.20+ with LAPACK bindings","SciPy 1.7+ for advanced decompositions","Python 3.9+"],"input_types":["matrix as nested list or 2D array (e.g., [[1,2],[3,4]])","decomposition type specification (eigen, svd, qr, cholesky, lu)","optional: tolerance thresholds for numerical stability"],"output_types":["scalar results (determinant, trace, condition number)","matrix results (inverse, transpose, decomposition factors)","eigenvalue/eigenvector pairs","decomposition components with reconstruction error metrics"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_10","uri":"capability://data.processing.analysis.pivot.table.creation.with.aggregation","name":"pivot-table-creation-with-aggregation","description":"Creates pivot tables from data with configurable row/column grouping and aggregation functions (sum, mean, count, min, max, std). Uses Pandas' pivot_table function under the hood, automatically handling missing values and providing multiple aggregation strategies. Returns a 2D table with grouped data and computed aggregates, useful for summarizing and cross-tabulating data.","intents":["Summarize sales data by product and region to identify top performers","Create a cross-tabulation of categorical variables to analyze relationships","Aggregate time-series data by time period and category","Compute multiple aggregations (sum, count, average) in a single pivot table","Prepare data for visualization or further analysis"],"best_for":["business analysts summarizing operational data","data scientists exploring data relationships","researchers cross-tabulating categorical variables","analysts preparing summary reports"],"limitations":["Pivot tables require explicit row/column specification; automatic grouping is not supported","Large pivot tables (many unique values in grouping columns) may become unwieldy","Aggregation functions are limited to standard operations (sum, mean, count, etc.); custom aggregations require preprocessing","Missing values in grouping columns create separate groups; no automatic imputation","Performance degrades with high-cardinality grouping columns (many unique values)"],"requires":["Pandas 1.3+ for pivot_table functionality","Python 3.9+"],"input_types":["2D data (list of dicts, dataframe, or nested list)","row grouping column(s) (string or list of strings)","column grouping column(s) (string or list of strings)","value column(s) to aggregate","aggregation function(s): 'sum' | 'mean' | 'count' | 'min' | 'max' | 'std'"],"output_types":["pivot table (2D structure with grouped rows/columns and aggregated values)","metadata (grouping columns, aggregation functions used, missing value handling)"],"categories":["data-processing-analysis","data-summarization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_11","uri":"capability://data.processing.analysis.unit.conversion.with.angle.support","name":"unit-conversion-with-angle-support","description":"Converts between units (currently focused on angle conversions: degrees ↔ radians) using mathematical constants and conversion formulas. Supports bidirectional conversion with automatic detection of input unit and output unit specification. Extensible architecture allows adding additional unit types (temperature, distance, etc.) without modifying core logic.","intents":["Convert angles from degrees to radians for trigonometric calculations","Convert radians back to degrees for human-readable output","Prepare angle data for mathematical functions that expect radians","Verify angle conversions in calculations"],"best_for":["engineers and scientists working with trigonometric functions","developers building physics or geometry simulations","researchers converting between angle representations"],"limitations":["Currently limited to angle conversions (degrees ↔ radians); other unit types not yet supported","No support for compound units (e.g., degrees/second)","Precision limited by floating-point arithmetic; very large angles may lose precision","No validation of input ranges; negative angles and angles > 360° are accepted without warning"],"requires":["Python 3.9+ (uses math.pi constant)"],"input_types":["numerical value (float)","source unit: 'degrees' | 'radians'","target unit: 'degrees' | 'radians'"],"output_types":["converted value (float)","metadata (source unit, target unit, conversion factor)"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_12","uri":"capability://data.processing.analysis.advanced.rounding.with.multiple.strategies","name":"advanced-rounding-with-multiple-strategies","description":"Provides multiple rounding strategies (round to nearest, floor, ceiling, truncate) using NumPy's rounding functions. Supports rounding to arbitrary decimal places or significant figures, with configurable tie-breaking behavior (banker's rounding, round-half-up). Returns both the rounded value and metadata about the rounding operation.","intents":["Round a number to a specific number of decimal places","Floor or ceiling a value for boundary calculations","Truncate decimal places without rounding","Round to significant figures for scientific notation","Handle tie-breaking consistently (banker's rounding vs round-half-up)"],"best_for":["financial calculations requiring precise rounding","scientific computations needing significant figures","developers implementing boundary conditions","anyone needing consistent rounding behavior"],"limitations":["Banker's rounding (round-half-to-even) may be unexpected for users familiar with round-half-up","Rounding to significant figures requires logarithmic computation; very small or very large numbers may lose precision","No support for custom rounding rules beyond standard strategies","Floating-point representation may introduce artifacts (e.g., 0.1 + 0.2 != 0.3)"],"requires":["NumPy 1.20+ for rounding functions","Python 3.9+"],"input_types":["numerical value (float)","rounding strategy: 'round' | 'floor' | 'ceil' | 'trunc'","optional: decimal places or significant figures"],"output_types":["rounded value","metadata (original value, rounding strategy, decimal places/significant figures used)"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_13","uri":"capability://data.processing.analysis.percentage.calculations.with.multiple.modes","name":"percentage-calculations-with-multiple-modes","description":"Computes percentage operations (percentage of a value, percentage increase/decrease, percentage change) using simple arithmetic with configurable output format (decimal, percentage string). Supports both forward calculations (what is 15% of 250?) and reverse calculations (250 is what percentage of 1000?). Returns both the numerical result and formatted percentage string.","intents":["Calculate what percentage one value represents of another","Compute percentage increase or decrease between two values","Find a percentage of a value (e.g., 15% of 250)","Calculate percentage change for trend analysis","Format results as percentage strings for display"],"best_for":["business analysts computing growth rates and margins","financial professionals calculating returns and changes","developers building calculators or dashboards","anyone needing quick percentage computations"],"limitations":["No built-in handling of percentage points vs percentages (e.g., 5% to 6% is 1 percentage point or 20% increase)","Division by zero (when base is 0) returns error; no automatic handling","Very large or very small percentages may be difficult to interpret","No support for compound percentage changes without explicit chaining"],"requires":["Python 3.9+ (basic arithmetic)"],"input_types":["numerical values (float)","operation type: 'of' | 'increase' | 'decrease' | 'change'","optional: output format ('decimal' | 'percentage')"],"output_types":["numerical result (float or percentage string)","metadata (operation type, base value, percentage value)"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_14","uri":"capability://data.processing.analysis.expression.evaluation.with.variable.substitution","name":"expression-evaluation-with-variable-substitution","description":"Evaluates mathematical expressions (e.g., 'x**2 + 3*x + 2') with variable substitution using SymPy's expression parser and NumPy for numerical evaluation. Supports both symbolic evaluation (returning expressions) and numerical evaluation (returning floats). Handles complex expressions with multiple variables, functions (sin, cos, exp, log), and constants (pi, e).","intents":["Evaluate a mathematical formula with specific variable values","Substitute variables into an expression and compute the result","Evaluate expressions with multiple variables and functions","Verify mathematical formulas by computing results","Build dynamic calculators that accept user-defined expressions"],"best_for":["developers building calculators or formula engines","educators creating interactive math tools","researchers evaluating mathematical models","anyone needing flexible expression evaluation"],"limitations":["Expression parsing is limited to SymPy's syntax; custom functions not in SymPy are not supported","No built-in security against malicious expressions; untrusted input should be validated","Very complex expressions may be slow to parse or evaluate","Floating-point precision limits numerical results; symbolic evaluation may be more accurate","No support for conditional expressions (if/else) or loops"],"requires":["SymPy 1.12+ for expression parsing","NumPy 1.20+ for numerical evaluation","Python 3.9+"],"input_types":["mathematical expression as string (e.g., 'x**2 + 3*x + 2')","variable substitutions as dict (e.g., {'x': 5})","optional: evaluation mode ('symbolic' | 'numerical')"],"output_types":["result (SymPy expression for symbolic, float for numerical)","metadata (variables used, functions detected, evaluation time)"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_15","uri":"capability://data.processing.analysis.linear.system.solver.with.stability.diagnostics","name":"linear-system-solver-with-stability-diagnostics","description":"Solves systems of linear equations (Ax = b) using NumPy's linear algebra solvers (LAPACK-backed), with automatic selection between direct solvers (LU decomposition) and iterative solvers for large sparse systems. Provides numerical stability diagnostics (condition number, residual norm) to assess solution reliability. Handles both square and overdetermined systems (least-squares).","intents":["Solve a system of linear equations Ax = b","Find the least-squares solution to an overdetermined system","Assess the numerical stability of a solution using condition numbers","Solve large sparse systems efficiently","Verify that a solution satisfies the original equations"],"best_for":["engineers solving structural or electrical systems","scientists fitting models to data","researchers solving differential equations numerically","developers building numerical simulation tools"],"limitations":["Direct solvers (LU) scale as O(n³); very large dense systems (>10k×10k) may be slow","Ill-conditioned systems (high condition number) may produce inaccurate solutions","Singular or near-singular matrices cause solver failure; no automatic regularization","Sparse matrix support requires explicit sparse matrix format; dense matrices are not automatically sparsified","No support for complex systems (Ax = b with complex A or b) beyond NumPy's native support"],"requires":["NumPy 1.20+ with LAPACK bindings","SciPy 1.7+ for sparse solvers (optional)","Python 3.9+"],"input_types":["coefficient matrix A (2D array or sparse matrix)","right-hand side b (1D or 2D array)","optional: solver type ('direct' | 'least_squares' | 'sparse')"],"output_types":["solution vector x","residual norm (||Ax - b||)","condition number (stability metric)","solver metadata (method used, iterations for iterative solvers)"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_2","uri":"capability://data.processing.analysis.financial.time.value.calculations","name":"financial-time-value-calculations","description":"Computes present value (PV), future value (FV), payment (PMT), internal rate of return (IRR), and net present value (NPV) using iterative numerical solvers (Newton-Raphson for IRR) and closed-form formulas for standard financial instruments. Handles multiple compounding frequencies and cash flow schedules, with automatic solver convergence detection and fallback strategies for edge cases.","intents":["Calculate the present value of a future cash flow given a discount rate","Determine the future value of an investment with compound interest at various frequencies","Find the internal rate of return (IRR) for a series of irregular cash flows","Compute net present value (NPV) for investment decision-making","Determine the periodic payment (PMT) for loans or annuities given principal and rate"],"best_for":["financial analysts building valuation models","loan officers computing amortization schedules","investment professionals evaluating project returns","business founders assessing investment viability"],"limitations":["IRR computation uses Newton-Raphson iteration; may not converge for cash flows with multiple sign changes or near-zero rates","Assumes fixed periodic cash flows for standard formulas; irregular schedules require explicit date-based NPV calculation","No built-in support for continuous compounding beyond standard frequency options (annual, semi-annual, quarterly, monthly, daily)","Numerical precision limited by floating-point arithmetic; very small discount rates or long time horizons may accumulate rounding errors"],"requires":["NumPy 1.20+ for numerical operations","SciPy 1.7+ for root-finding (Newton-Raphson)","Python 3.9+"],"input_types":["principal amount (float)","interest rate as decimal (e.g., 0.05 for 5%)","time period in years (float)","compounding frequency (annual, semi-annual, quarterly, monthly, daily)","cash flow arrays for NPV/IRR calculations"],"output_types":["monetary value (PV, FV, PMT, NPV)","percentage (IRR as decimal)","convergence metadata (iterations, tolerance achieved)","amortization schedule (optional)"],"categories":["data-processing-analysis","financial-mathematics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_3","uri":"capability://automation.workflow.batch.execution.with.dependency.resolution","name":"batch-execution-with-dependency-resolution","description":"Chains multiple calculations in a single MCP request using a directed acyclic graph (DAG) execution model with automatic dependency resolution and optional parallel execution. Supports referencing prior operation results via `$operation_id.result` syntax, with the engine resolving dependencies, executing independent operations concurrently, and returning a consolidated result set. Achieves 90-95% token reduction by eliminating round-trip overhead.","intents":["Execute a multi-step financial model (e.g., calculate PV, then use it in NPV calculation) in a single request","Chain statistical analyses where later steps depend on earlier aggregations","Build complex mathematical pipelines (e.g., matrix decomposition → eigenvalue extraction → normalization) without intermediate LLM calls","Reduce token usage for workflows with many sequential calculations by batching them"],"best_for":["developers building complex financial or scientific models","teams optimizing token usage in LLM-driven applications","researchers running multi-step data transformation pipelines","anyone needing deterministic, reproducible multi-step calculations"],"limitations":["DAG must be acyclic; circular dependencies are detected and rejected","Parallel execution benefits only independent operations; sequential dependencies still block","Error in any operation halts the entire batch; no partial result recovery","Result references use string-based syntax ($operation_id.result); no compile-time validation of reference validity","Maximum batch size and operation count may be limited by MCP server configuration"],"requires":["MCP client supporting batch_execute tool","Familiarity with operation ID referencing syntax","Python 3.9+ (server-side)"],"input_types":["array of operation objects, each with: id (string), tool (string), parameters (object)","operation parameters may reference prior results via $operation_id.result syntax"],"output_types":["consolidated result object with all operation outputs keyed by operation ID","execution metadata (order, parallelization info, total latency)","error details if any operation fails"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_4","uri":"capability://tool.use.integration.output.mode.controlled.response.verbosity","name":"output-mode-controlled-response-verbosity","description":"Provides five configurable output modes (full, compact, minimal, value, final) on every tool that control response verbosity and token efficiency. The engine strips metadata, null fields, and structural information based on mode selection, enabling developers to optimize token usage for their specific use case. Modes range from full debugging output (0% reduction) to terminal-only results (95% reduction).","intents":["Reduce token usage for production calculations by returning only the final value","Debug calculations by requesting full metadata and intermediate steps","Chain calculations efficiently by using 'value' mode for intermediate results","Balance between readability and token efficiency with 'compact' mode","Extract only the primary result for simple calculations without structural overhead"],"best_for":["developers optimizing token usage in cost-sensitive applications","debugging workflows where full context is needed","production systems where only final results matter","chained calculations where intermediate results feed into later steps"],"limitations":["Minimal/value/final modes lose error context and precision metadata; debugging becomes harder","Output mode selection is per-tool; no global default across batch operations","Stripping metadata may make results ambiguous if multiple values are computed (e.g., both mean and median)","No custom output templates; only predefined modes are available"],"requires":["MCP client passing output_mode parameter to tool calls","Understanding of what metadata each mode strips"],"input_types":["output_mode parameter: 'full' | 'compact' | 'minimal' | 'value' | 'final'"],"output_types":["full: complete response with all metadata (baseline)","compact: null fields removed, whitespace minimized (~20-30% reduction)","minimal: primary values only, metadata stripped (~60-70% reduction)","value: normalized {value: X} structure (~70-80% reduction)","final: terminal result only for sequential chains (~95% reduction)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_5","uri":"capability://data.processing.analysis.statistical.analysis.with.outlier.detection","name":"statistical-analysis-with-outlier-detection","description":"Computes comprehensive statistical measures (mean, median, std, min, max, sum, quartiles) and detects outliers using IQR (interquartile range) or Z-score methods. Leverages NumPy and Pandas for efficient vectorized computation, with automatic handling of missing values and configurable outlier thresholds. Returns both summary statistics and detailed outlier identification with flagging.","intents":["Analyze a dataset to understand its distribution, central tendency, and spread","Identify outliers in a dataset using statistical methods (IQR or Z-score)","Compute quartiles and percentiles for understanding data distribution","Generate a statistical summary for exploratory data analysis (EDA)","Detect anomalies in time-series or measurement data"],"best_for":["data analysts performing exploratory data analysis","quality assurance teams detecting anomalies in measurements","researchers understanding dataset characteristics before modeling","business analysts identifying unusual transactions or patterns"],"limitations":["Outlier detection assumes univariate data; multivariate outliers require additional methods (Mahalanobis distance, isolation forests)","IQR method may be too conservative for small datasets (n < 30); Z-score method assumes normality","Missing values are excluded from computation; no imputation strategies built-in","Quartile computation methods vary (linear, lower, higher, midpoint); default uses linear interpolation"],"requires":["NumPy 1.20+ for vectorized computation","Pandas 1.3+ for missing value handling","Python 3.9+"],"input_types":["array of numerical values (list or 1D array)","outlier detection method: 'iqr' | 'zscore'","optional: outlier threshold (IQR multiplier or Z-score cutoff)"],"output_types":["summary statistics object (mean, median, std, min, max, sum, quartiles)","outlier flags (boolean array or indices)","outlier values with their statistical distance from center","distribution shape metadata (skewness, kurtosis)"],"categories":["data-processing-analysis","statistical-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_6","uri":"capability://data.processing.analysis.array.operations.with.element.wise.broadcasting","name":"array-operations-with-element-wise-broadcasting","description":"Performs element-wise array operations (add, subtract, multiply, divide, power) using NumPy's broadcasting rules, enabling operations on arrays of different shapes without explicit reshaping. Automatically aligns dimensions according to NumPy broadcasting semantics, making it easy to combine scalars with arrays or arrays of different shapes. Returns results with shape and dtype metadata.","intents":["Add or multiply arrays of different shapes using broadcasting rules","Perform element-wise operations on large arrays efficiently","Scale arrays by a scalar or apply element-wise transformations","Combine multiple arrays in mathematical expressions without manual reshaping"],"best_for":["data scientists performing vectorized computations","engineers working with multi-dimensional data","developers building numerical pipelines"],"limitations":["Broadcasting rules can be counterintuitive; misaligned shapes may produce unexpected results","No automatic type promotion; mixing integer and float arrays may truncate results","Very large arrays (>1GB) may exceed memory limits","No built-in validation of semantic correctness; broadcasting is purely shape-based"],"requires":["NumPy 1.20+"],"input_types":["arrays as nested lists or 1D/2D arrays","operation type: 'add' | 'subtract' | 'multiply' | 'divide' | 'power'","optional: dtype specification"],"output_types":["result array with shape and dtype metadata","broadcasting trace (which dimensions were aligned)"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_7","uri":"capability://data.processing.analysis.array.aggregation.with.weighted.operations","name":"array-aggregation-with-weighted-operations","description":"Computes aggregations (sumproduct, weighted average, dot product) on arrays using NumPy's optimized routines. Supports weighted aggregations where each element contributes proportionally to a weight, enabling calculations like weighted averages, weighted sums, and dot products. Handles edge cases (zero weights, empty arrays) gracefully with configurable behavior.","intents":["Calculate a weighted average where some values matter more than others","Compute a dot product between two vectors for similarity or projection","Calculate sumproduct for correlation or covariance-like operations","Aggregate data with importance weights (e.g., portfolio returns weighted by asset allocation)"],"best_for":["financial analysts computing weighted portfolio returns","data scientists calculating weighted metrics","engineers computing dot products for linear algebra","researchers aggregating data with importance weights"],"limitations":["Weighted operations assume weights sum to 1 or are normalized; unnormalized weights may produce unexpected results","Zero or negative weights may cause numerical issues; no automatic validation","Dot product requires arrays of compatible shapes; broadcasting is not applied","No built-in support for masked arrays (NaN handling requires preprocessing)"],"requires":["NumPy 1.20+"],"input_types":["array of values (1D or 2D)","optional: array of weights (same shape as values)","aggregation type: 'sumproduct' | 'weighted_average' | 'dot_product'"],"output_types":["scalar result (weighted average, dot product, sumproduct)","metadata (effective weight, normalization factor)"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_8","uri":"capability://data.processing.analysis.array.transformation.with.normalization.and.scaling","name":"array-transformation-with-normalization-and-scaling","description":"Transforms arrays using normalization (min-max scaling to [0,1]), standardization (z-score to mean=0, std=1), log transformation, and custom scaling. Applies transformations element-wise or across specified axes, with automatic handling of edge cases (zero variance, negative values for log). Returns both transformed arrays and transformation parameters (min, max, mean, std) for inverse transformation.","intents":["Normalize data to [0,1] range for machine learning models","Standardize features to zero mean and unit variance (z-score normalization)","Apply log transformation to reduce skewness in data","Scale arrays by a factor or to a target range","Prepare data for algorithms sensitive to feature scaling (KNN, SVM, neural networks)"],"best_for":["machine learning engineers preparing features for models","data scientists performing data preprocessing","researchers normalizing measurements across different scales","analysts preparing data for visualization"],"limitations":["Min-max scaling is sensitive to outliers; extreme values compress the range for normal data","Standardization assumes data is approximately normal; skewed distributions may not normalize well","Log transformation requires positive values; zero or negative values cause errors","Transformation parameters (min, max, mean, std) must be saved separately for inverse transformation; no built-in inverse function"],"requires":["NumPy 1.20+"],"input_types":["array of numerical values","transformation type: 'normalize' | 'standardize' | 'log' | 'scale'","optional: axis for transformation (None for global, 0 for rows, 1 for columns)","optional: scale factor or target range"],"output_types":["transformed array","transformation parameters (min, max, mean, std) for reproducibility","metadata (original range, transformed range)"],"categories":["data-processing-analysis","machine-learning-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apetta-vibe-math-mcp__cap_9","uri":"capability://data.processing.analysis.correlation.matrix.computation.with.multiple.methods","name":"correlation-matrix-computation-with-multiple-methods","description":"Computes correlation matrices using Pearson (linear correlation), Spearman (rank-based correlation), or Kendall (tau) methods. Leverages SciPy's optimized implementations for efficient computation on large datasets, with automatic handling of missing values and NaN propagation. 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