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Scientific computation: named tensors, matrix algebra and vector operations, fully local.
About
Scientific computation: named tensors, matrix algebra and vector operations, fully local.
Security Report
scicompute is a pure local matrix and vector math server with an in-process tensor store, no network calls, no shell or eval usage, and no hardcoded secrets. Its only capabilities are in-memory state and stdio transport, which match its stated purpose; the minor observations are that it requires no authentication (appropriate for a local stdio server) and that the tensor store grows without limits. The server scores high because it has no security findings of significance. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 2 high severity). Package verification found 1 issue.
8 files analyzed · 8 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-cloudcorprecords-scicompute": {
"args": [
"-y",
"@reneturcios/scicompute"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
scicompute
scicompute is a scientific-computation server built around one idea: a named, in-process tensor store. You create tensors under names, then run matrix algebra and vector operations by referring to those names. Everything is computed locally in pure TypeScript — no network, no external math library. One binary serves a command-line interface and a stdio MCP server.
This build is a labelled core subset of the documented tool set: tensor storage, linear algebra, and vector operations (14 tools). Eigen/SVD/QR decompositions, the vector-calculus tools, basis changes, and plotting from the wider documented set are not included; see the build report for the full omission list.
Install
npx @reneturcios/scicompute create-tensor T 2,2 1,2,3,4
Or run the container image:
docker run -i ghcr.io/cloudcorprecords/scicompute
The container defaults to the MCP server (see below). Pass CLI arguments to use it as a CLI instead.
CLI
scicompute create-tensor A 2,2 4,7,2,6
scicompute view-tensor A
scicompute determinant A
scicompute --version
The store is in-process, so a single CLI invocation holds its
tensors only for its own lifetime; chained operations over several
tensors belong to one MCP session, where the store persists across
tool calls. Exit codes: 0 on success, 1 for a computation error
(such as a shape mismatch or a singular matrix), 2 for usage
errors.
Subcommands: create-tensor, view-tensor, delete-tensor,
add, subtract, multiply, scale (--no-in-place to leave
the stored matrix untouched), transpose, determinant, rank,
inverse, dot, cross, project, and mcp.
MCP server
npx -y @reneturcios/scicompute mcp
MCP client configuration:
{
"mcpServers": {
"scicompute": {
"command": "npx",
"args": ["-y", "@reneturcios/scicompute", "mcp"]
}
}
}
Tools
| Tool | Input | What it returns |
|---|---|---|
create_tensor | name, shape, values (all required) | The stored tensor: name, shape, flat values, and nested data. values length must equal the shape product. |
view_tensor | name (required) | The stored tensor under that name. |
delete_tensor | name (required) | Confirmation that the tensor was deleted. |
add_matrices | a, b (required), result (optional) | The element-wise sum matrix; stored under result when given. |
subtract_matrices | a, b (required), result (optional) | The element-wise difference matrix. |
multiply_matrices | a, b (required), result (optional) | The matrix product. |
scale_matrix | name, factor (required), in_place (optional) | The scaled matrix. In place by default; stored matrix is untouched when in_place is false. |
transpose | name (required), result (optional) | The transposed matrix. |
determinant | name (required) | The determinant of a square matrix. |
rank | name (required) | The matrix rank, from row-echelon reduction. |
matrix_inverse | name (required), result (optional) | The inverse of a square, non-singular matrix. |
vector_dot_product | a, b (required) | The dot product of two stored vectors. |
vector_cross_product | a, b (required) | The cross product of two stored 3-vectors. |
vector_project | a, b (required) | The projection of vector a onto vector b. |
Algebra and vector operands are tensor names, not raw data. Matrices are 2-entry shapes and vectors are 1-entry shapes; shape mismatches, missing tensors, non-square determinant/inverse requests, singular inverses, and projections onto the zero vector are all errors rather than silent zeros.
Development
npm install
npm run build # tsc -> dist/
npm test # node test runner over test/*.test.ts, no network
npm run dev # run the CLI from source via tsx
License
MIT — see LICENSE.
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