Scientific Computation MCP

[](https://mseep.ai/app/aman-amith-shastry-scientific-computation-mcp) # Scientific Computation MCP [](https://smithery.ai/server/@Aman-Amith-Shastry/scientific_computation_mcp) [](https://mseep.ai/app/5927ad38-70f6-4f5b-9778-e61ec902d735) [](https://lobehub.com/mcp/aman-amith-shastry-scientific_computation_mcp) ## Installation Guide ### Claude Desktop Open Claude Desktop's configuration file (claude_desktop_config.json) and add the following: - Mac/Linux: ```json { "mcpServers": { "numpy_mcp": { "command": "npx", "args": [ "-y", "@smithery/cli@latest", "run", "@Aman-Amith-Shastry/scientific_computation_mcp", "--key", "<YOUR_SMITHERY_API_KEY>" ] } } } ``` - Windows: ```json { "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: ```commandline 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: ```commandline 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.