{"slug":"Aman-Amith-Shastry/scientific_computation_mcp","name":"Scientific Computation MCP","description":"Provides tools for scientific computation, including tensor storage, linear algebra, vector calculus, and visualization.","category":"development","tags":[],"official":false,"stars":2,"transport":"http","install":[{"cmd":"npx -y @smithery/cli@latest","imports":[]}],"tools":[{"name":"create_tensor","description":"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."},{"name":"view_tensor","description":"Display the contents of a tensor from the store."},{"name":"delete_tensor","description":"Deletes a tensor based on its name in the tensor store."},{"name":"add_matrices","description":"Adds two matrices with the provided names, if compatible."},{"name":"subtract_matrices","description":"Subtracts two matrices with the provided names, if compatible."},{"name":"multiply_matrices","description":"Multiplies two matrices with the provided names, if compatible."},{"name":"scale_matrix","description":"Scales a matrix of the provided name by a certain factor, in-place by default."},{"name":"matrix_inverse","description":"Computes the inverse of the matrix with the provided name."},{"name":"transpose","description":"Computes the transpose of the inverse of the matrix of the provided name."},{"name":"determinant","description":"Computes the determinant of the matrix of the provided name."},{"name":"rank","description":"Computes the rank (number of pivots) of the matrix of the provided name."},{"name":"compute_eigen","description":"Calculates the eigenvectors and eigenvalues of the matrix of the provided name."},{"name":"qr_decompose","description":"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."},{"name":"svd_decompose","description":"Computes the Singular Value Decomposition of the matrix of the provided name."},{"name":"find_orthonormal_basis","description":"Finds an orthonormal basis for the matrix of the provided name. The vectors returned are all pair-wise orthogonal and are of unit length."},{"name":"change_basis","description":"Computes the matrix of the provided name in the new basis."},{"name":"vector_project","description":"Projects a vector in the tensor store to the specified vector in the same vector space."},{"name":"vector_dot_product","description":"Computes the dot product of two vectors in the tensor stores based on their provided names."},{"name":"vector_cross_product","description":"Computes the cross product of two vectors in the tensor stores based on their provided names."},{"name":"gradient","description":"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) = ...)."},{"name":"curl","description":"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]\")."},{"name":"divergence","description":"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]\")."},{"name":"laplacian","description":"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."},{"name":"directional_deriv","description":"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."},{"name":"plot_vector_field","description":"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."},{"name":"plot_function","description":"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."}],"env_vars":["YOUR_SMITHERY_API_KEY"],"auth_type":"none","github":"https://github.com/Aman-Amith-Shastry/scientific_computation_mcp","homepage":"","server_url":"","status":"active","source":"mcpservers.org","updated_at":"Mon May 25"}