Scientific Computation MCP

The server speaks streamable HTTP. Add it from Smithery with the Smithery CLI (Node 20+):

npm install -g smithery@latest smithery mcp add @aman-amith-shastry/scientific_computation_mcp --client claude

The namespace is lowercase. Smithery's registry lookup is case-sensitive, and the mixed-case spelling resolves to an empty record with no tools rather than failing outright, so a capitalized name looks like a server with no capabilities.

Swap--client cursorfor Cursor, or drop--clientto add it as a remote Smithery connection. Restart the client afterwards so it picks up the server.

The MCP endpoint is athttp://localhost:8081/mcpand a liveness probe athttp://localhost:8081/health. Environment overrides:PORT,HOST,MCP_PATH,ALLOWED_ORIGINS,LOG_LEVEL.

Smithery no longer builds or hosts containers — servers are published either as a URL that Smithery's gateway proxies to, or as an MCPB bundle for local stdio. This server is published by URL, so the container runs on any host that can serve HTTPS.

docker build -t scientific-computation-mcp . docker run -p 8081:8081 -e PORT=8081 scientific-computation-mcp

- One instance.Tensors live in process memory between tool calls, so scaling past a single replica splits the store and breakscreate_tensor→view_tensorflows.
- Sessions must survive.The transport runs stateful (stateless_http=False) and the tensor store is keyed per MCP session, which is what keeps concurrent users from reading each other's tensors.

render.yamldeploys the Dockerfile as a single free-plan web service. In the Render dashboard:New → Blueprint, then select this repo. Render injectsPORT, terminates TLS, and probes/health; no other configuration is required.

curl -sS -o /dev/null -w '%{http_code}\n' https://<your-host>/health smithery mcp publish "https://<your-host>/mcp" -n @aman-amith-shastry/scientific_computation_mcp

- 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 thegradienttool. If the input is a vector field, it must be input in the same manner as thecurl/divergencetools.
- directional_deriv: Computes the directional derivative of a function in a given directionuBy default, the tool normalizesubefore computing the directional derivative, as specified by theunitparameter.

- 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 thegradient`tool. Only the variables x and y can be used.

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