Scientific Computation MCP

# Installation Guide

# # Claude Desktop

Open Claude Desktop's configuration file (claude_desktop_config.json) and add the following:

• Mac/Linux:

{
  "mcpServers": {
    "numpy_mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "@Aman-Amith-Shastry/scientific_computation_mcp",
        "- -key",
        "<YOUR_SMITHERY_API_KEY>"
      ]
    }
  }
}

• Windows:

{
  "mcpServers": {
    "numpy_mcp": {
      "command": "cmd",
      "args": [
        "/c",
        "npx",
        "-y",
        "@smithery/cli@latest",
        "run",
        "@Aman-Amith-Shastry/scientific_computation_mcp",
        "- -key",
        "<YOUR_SMITHERY_API_KEY>"
      ]
    }
  }
}

Or alternatively, run the following command:

npx -y @smithery/cli@latest install @Aman-Amith-Shastry/scientific_computation_mcp - -client claude - -key <YOUR_SMITHERY_API_KEY>

Restart Claude to load the server properly

# # Cursor

If you prefer to access the server through Cursor instead, then run the following command:

npx -y @smithery/cli@latest install @Aman-Amith-Shastry/scientific_computation_mcp - -client cursor - -key <YOUR_SMITHERY_API_KEY>

# Components of the Server

# # Tools

# # # Tensor storage

• create_tensor: Creates a new tensor based on a given name, shape, and values, and adds it to the tensor store. For the purposes of this server, tensors are vectors and matrices.
• view_tensor: Display the contents of a tensor from the store .
• delete_tensor: Deletes a tensor based on its name in the tensor store.

# # # Linear Algebra

• add_matrices: Adds two matrices with the provided names, if compatible.
• subtract_matrices: Subtracts two matrices with the provided names, if compatible.
• multiply_matrices: Multiplies two matrices with the provided names, if compatible.
• scale_matrix: Scales a matrix of the provided name by a certain factor, in-place by default.
• matrix_inverse: Computes the inverse of the matrix with the provided name.
• transpose: Computes the transpose of the inverse of the matrix of the provided name.
• determinant: Computes the determinant of the matrix of the provided name.
• rank: Computes the rank (number of pivots) of the matrix of the provided name.
• compute_eigen: Calculates the eigenvectors and eigenvalues of the matrix of the provided name.
• qr_decompose: Computes the QR factorization of the matrix of the provided name. The columns of Q are an orthonormal basis for the image of the matrix, and R is upper triangular.
• svd_decompose: Computes the Singular Value Decomposition of the matrix of the provided name.
• find_orthonormal_basis: Finds an orthonormal basis for the matrix of the provided name. The vectors returned are all pair-wise orthogonal and are of unit length.
• change_basis: Computes the matrix of the provided name in the new basis.

# # # Vector Calculus

• vector_project: Projects a vector in the tensor store to the specified vector in the same vector space
• vector_dot_product: Computes the dot product of two vectors in the tensor stores based on their provided names.
• vector_cross_product: Computes the cross product of two vectors in the tensor stores based on their provided names.
• gradient: Computes the gradient of a multivariable function based on the input function. Example call: gradient("x^2 + 2xyz + zy^3"). Do NOT include the function name (like f(x, y, z) = ...`).
• curl: Computes the curl of a vector field based on the input vector field. The input string must be formatted as a python list. Example call: curl("[3xy, 2z^4, 2y]"").
• divergenceComputes the divergence of a vector field based on the input vector field. The input string must be formatted as a python list. Example call: divergence("[3xy, 2z^4, 2y]"").
• laplacianComputes the laplacian of a scalar function (as the divergence of the gradient) or a vector field (where a component-wise laplacian is computed). If a scalar function is the input, it must be input in the same format as in the gradient tool. If the input is a vector field, it must be input in the same manner as the curl/divergence tools.
• directional_deriv: Computes the directional derivative of a function in a given direction u By default, the tool normalizes u before computing the directional derivative, as specified by the unit parameter.

# # # Visualization

• plot_vector_field: Plots a vector field (specified in the same format as in the curl/divergence functions). Currently, only 3d vector fields are supported. A 2d png perspective image of the vector field is returned. By default, the bounds of the graph are from -1 to 1 on each axis.
• plot_function: Plots a function in 2d or 3d (based on the input variables), specified in the same format as in the gradient tool. Only the variables x and y can be used.