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io.github.daedalus/mcp-numpy

Official registry Other

An MCP server that exposes NumPy functionality

Source code Documentation

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

PYPI package mcp-numpy
Claude Code 
claude mcp add mcp-numpy -- uvx mcp-numpy==0.1.0
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "mcp-numpy": {
            "command": "uvx",
            "args": [
                "mcp-numpy==0.1.0"
            ]
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. mcp-numpy
  2. Install
  3. Usage
  4. Development

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

Frequently asked questions

What is io.github.daedalus/mcp-numpy?

An MCP server that exposes NumPy functionality

How do I install io.github.daedalus/mcp-numpy?

It runs on your machine. In Claude Code: claude mcp add mcp-numpy -- uvx mcp-numpy==0.1.0. For other clients, paste the JSON configuration from this page into their MCP settings.

Does io.github.daedalus/mcp-numpy need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is io.github.daedalus/mcp-numpy open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains io.github.daedalus/mcp-numpy?

It is published by daedalus. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.