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mcp-numpy

mcp-numpy

Local
github.com/daedalusScience & Education0Updated 27w ago
Repository

About mcp-numpy

mcp-numpy exposes NumPy array, math, and statistics operations as an MCP server for MCP clients. Its tools cover common array manipulation, linear algebra, and random-number tasks.

Key features

  1. 01Create and reshape arrays
  2. 02Run math and statistics operations
  3. 03Perform linear algebra
  4. 04Generate and manipulate random values
  5. 05Inspect array properties and types

Use cases

  1. 01Run array computations through a client
  2. 02Calculate summary statistics and correlations
  3. 03Solve linear systems and find matrix eigenvalues

MCP support

As an MCP server, it provides clients with NumPy tools for array creation and manipulation, math, statistics, and linear algebra.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "daedalus-mcp-numpy": {
      "command": "uvx",
      "args": [
        "mcp-numpy"
      ]
    }
  }
}
Run commanduvx mcp-numpy

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypimcp-numpy0.1.0

Tools

72

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
dataarrayYesA Python list containing the array elements.
dtypestringNoThe data type of the array (default: "float64"). Common values: "int32", "int64", "float32", "float64", "complex128".

Details

From the project README

mcp-numpy

An MCP server that exposes NumPy functionality

PyPI Python Coverage Ruff

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

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