mcp-numpy

An MCP server that exposes NumPy functionality

Install

pip install mcp-numpy

Usage

As an MCP Server

To use with Claude Desktop or other MCP clients, add to your mcp.json:

{
  "mcpServers": {
    "mcp-numpy": {
      "command": "mcp-numpy"
    }
  }
}

Available Tools

The server exposes the following NumPy functionality as MCP tools:

Array Creation

• np_array - Create a NumPy array

• np_zeros - Create zeros array

• np_ones - Create ones array

• np_full - Create array filled with value

• np_arange - Create array with range

• np_linspace - Create evenly spaced array

• np_eye - Create identity matrix

• np_diag - Create diagonal array

Array Manipulation

• np_reshape - Reshape array

• np_transpose - Transpose array

• np_concatenate - Concatenate arrays

• np_split - Split array

• np_tile - Tile array

• np_repeat - Repeat elements

• np_squeeze - Remove single-dimensional entries

• np_flatten - Flatten array

Mathematical Operations

• np_sum, np_mean, np_std, np_var - Summary statistics

• np_min, np_max, np_argmin, np_argmax - Min/max operations

• np_dot, np_matmul, np_cross - Matrix operations

• np_trace, np_cumsum, np_cumprod, np_diff - Array operations

Linear Algebra

• np_inv - Matrix inverse

• np_det - Matrix determinant

• np_eig - Eigenvalues and eigenvectors

• np_svd - Singular value decomposition

• np_solve - Solve linear system

• np_linalg_norm - Matrix/vector norm

Random

• np_rand - Random floats

• np_randn - Random normal

• np_randint - Random integers

• np_random_choice - Random choice

• np_shuffle - Shuffle array

Statistics

• np_percentile, np_quantile - Percentiles/quantiles

• np_histogram - Histogram

• np_correlate, np_corrcoef - Correlation

Element-wise Math

• np_add, np_subtract, np_multiply, np_divide - Arithmetic

• np_power, np_mod - Power and modulo

• np_sqrt, np_abs - Basic math

• np_exp, np_log, np_log10 - Logarithms

• np_sin, np_cos, np_tan - Trigonometry

• np_arcsin, np_arccos, np_arctan - Inverse trig

• np_sinh, np_cosh, np_tanh - Hyperbolic

Array Properties

• np_shape, np_ndim, np_size, np_dtype - Properties

• npastype - Type conversion

Development

git clone https://github.com/daedalus/mcp-numpy.git
cd mcp-numpy
pip install -e ".[test]"

# run tests
pytest

# format
ruff format src/ tests/

# lint
ruff check src/ tests/

# type check
mypy src/

mcp-name: io.github.daedalus/mcp-numpy