Mcp Numpy

io.github.daedalusPublished by GitHub account daedalusv0.1.0Updated Oct 24, 2026

An MCP server that exposes NumPy functionality

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Overview

AI-generated overview

A local MCP server that exposes NumPy array creation, manipulation, math, linear algebra, random and statistics functions as tools for an assistant.

What you can do
Create arrays and matrices (zeros, ones, ranges, identity, diagonal, filled values). Reshape, transpose, concatenate, split, tile, repeat, squeeze and flatten arrays. Compute summary statistics, min/max, dot and matrix products, traces, cumulative sums and differences. Run linear algebra such as inverse, determinant, eigenvalues, SVD, linear solves and norms. Generate random numbers and shuffles, and compute percentiles, histograms and correlations. Apply element-wise arithmetic, powers, roots, logs, trigonometry and type conversion, and inspect shape, dimensions, size and dtype.
When to use it
Useful when you want an assistant to perform numerical array work — quick matrix calculations, statistics, linear algebra or random sampling — without writing and running Python yourself. Suited to exploratory numeric computation on small to moderate data passed through tool calls.
Requirements
Runs as a local process installed from the PyPI package mcp-numpy (pip install mcp-numpy) and configured in the MCP client with the command mcp-numpy. No authentication, environment variables or headers are declared. Desktop client only.
Before you install
Arrays are passed through tool calls, so large inputs may be slow or hit size limits. Random tools produce non-reproducible results unless a seed is supplied. No credentials or write access to external data are involved.

Installation

In SourceWeft

  1. Open Mcp Numpy in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

mcp-numpy

An MCP server that exposes NumPy functionality

[PyPI] [Python] [Coverage] [Ruff]

Install

bash
pip install mcp-numpy

Usage

As an MCP Server

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

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

bash
git clone https://github.com/daedalus/mcp-numpy.gitcd mcp-numpypip install -e ".[test]"
# run testspytest
# formatruff format src/ tests/
# lintruff check src/ tests/
# type checkmypy src/

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

Source: README.md at commit 18fa3da

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.1.0LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts