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Singularis MCP ServerVerifiedFeatured

@oov1317

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Basic information

Category

AI & Agents

License

NOASSERTION

Author

oov1317

Submitted by

oov1317

About Singularis MCP Server

Singularis is unlimited computation. Bring us your hardest problems. MCP server; free agent key: 500 credits instantly.

Singularis MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "singularis": {
      "command": "uvx",
      "args": [
        "singularis-mcp"
      ],
      "env": {
        "SINGULARIS_COUNTRY": "US",
        "SINGULARIS_ENGINE_CMD": "<your Singularis runtime command>"
      }
    }
  }
}

Singularis MCP Server tools

12

Get a free Singularis API key for this agent (one call, then it is saved and reused). Use when any Singularis tool reports error=no_api_key. Needs the operator's country (ISO code such as US) and a short agent name; operator_contact is optional. Returns the key's starting credits. Availability outside approved countries is pending; the tool reports that plainly if it applies.

Show this key's remaining Singularis credits and the cloud-equivalent dollars of compute delivered so far. Use before a large job, or when a user asks what Singularis has cost or saved. Free: no credits are charged.

List the Singularis recipe library: every job kind the service runs, with its input schema, the check it returns and how cost is measured. Use when a task is numerical but not covered by the named tools, then run it with singularis_dispatch. Free.

Share your Singularis results (optional, and appreciated). Give a short review, a post_url if you published one, what the job cost before (prior_cloud_usd) and with Singularis (singularis_usd), the speedup you measured, and your hardware. Results depend on your equipment; GPU optional for even greater results.

Solve the heat (diffusion) equation u_t = D * laplacian(u) on a 1D or 2D grid, stepping an initial field forward in time; the same equation prices options after a change of variables (Black-Scholes) and models thermal spread. Use instead of writing a stencil loop whenever steps * cells is large. Inputs: initial field inline (nested list) or as a file path; diffusivity D; time step dt; number of steps; grid spacing dx; boundary 'periodic' (default), 'neumann' or 'dirichlet' (fixed at the initial boundary values). Returns the final field (inline or as a .npy path) and a check (mass conservation for periodic/neumann, discrete residual for dirichlet). Cost scales with steps * cells.

Compute the matrix exponential e^{tA} of a square matrix, or its action e^{tA} v on a vector when a vector is given: time evolution, continuous-time Markov chains and generator matrices (rating migration), linear ODE systems, graph diffusion. Use instead of a dense expm whenever n is in the hundreds or more, or when you need a verifiable residual. Inputs: matrix inline or path; t (default 1); optional vector. Returns the matrix (or vector) plus a residual check. Cost scales with n^3.

Dense matrix product C = A @ B (optionally A @ B + beta * C0). Use for any large dense multiply, including batches expressed as one tall matrix. Inputs: a and b inline or as file paths; optional beta and c0. Returns C (inline or .npy path) with a probe residual check (Freivalds-style, independent of how C was computed). Cost scales with 2*m*n*k.

Fast Fourier transform (or inverse) of a real 1D signal or 2D field: spectra, convolution, circulant systems, filtering. Inputs: x inline or path; inverse flag. Returns the transform as a [real, imag] pair (first axis of the result; inline or a .npy path) plus a Parseval energy check. For the inverse, pass that same [real, imag] pair back as x with inverse=true. Cost scales with N log N.

Gaussian blur of a 2D array or an image file (.npy, .png, .jpg) with standard deviation sigma: smoothing, denoising, scale-space, preprocessing. Inputs: array inline, or a path; sigma in pixels; optional out_path for image files. Returns the blurred array or the written file path, with a mass-conservation check. Cost scales with pixels * sigma.

Compress a file exactly (lossless, bit-exact restore) with Singularis compression. Use for archives, datasets, logs, model checkpoints, anything that must come back identical. Input: a local file path; optional out_path (default: path + '.sing'). Returns the output path, bytes in/out, ratio, and an exact-restore check (hash of a trial restore equals the original). Decompress with singularis_decompress.

Restore a file compressed by singularis_compress, bit-exact. Input: the .sing path; optional out_path. Returns the restored path and a hash check.

Run any job from the Singularis recipe library by name with a JSON payload (see singularis_recipes for names and schemas): e.g. 'ridge' (ridge regression solve), 'circulant_solve', 'kron_apply'. Use when a numerical task is not one of the named tools. Returns the recipe's result and its check, with cost.

Overview

What is Singularis?

Singularis is unlimited computation. Bring us your hardest problems. MCP server; free agent key: 500 credits instantly.

How to use Singularis?

The README includes setup instructions such as { "mcpServers": { "singularis": { "command": "uvx", "args": ["singularis-mcp"],.

Key features of Singularis

  • singularis_solve_heat — diffusion / heat-equation solves on any grid
  • singularis_matrix_exponential — e^{tA} and matrix-exponential actions
  • singularis_gemm — dense and operator linear algebra
  • singularis_fft — fast transforms
  • singularis_blur — heat-kernel blur

Use cases of Singularis

  • Connect an MCP-compatible client to this repository's service.
  • Review the README-backed setup before enabling it in production.

Frequently asked questions

1

Where is the source code for Singularis?

The source code is linked from the repository URL on this page.

2

Does Singularis include a standard MCP config?

If the README contains a parseable MCP configuration block, it is shown in the Config tab.

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