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]"")``.
• ``divergence`Computes 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]"")``.
• ``laplacian`Computes 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.