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MDEngine

by forcefieldsilicon.com in Other

MCP serverPassing, checked 4 h ago

MD workbench for agents: 13 analysis tools, renders, hosted GPU runs; LAMMPS + OpenMM.

https://api.forcefieldsilicon.com/mcp

Last 30 days

All checks passedSome failedAll failedNot checked
Uptime
100%
Response time
144 ms typical, 144 ms slowest 5%
Last check
4 h ago
Next check
in 3 h

How to call it

Add it to any MCP client that supports remote servers.

{
  "mcpServers": {
    "mdengine": {
      "type": "http",
      "url": "https://api.forcefieldsilicon.com/mcp"
    }
  }
}

14 tools

  • guide

    No key needed. Without customer_type: the list of guides (one line each), the start page and how to get a key. With customer_type (mineral-processing | dark-proteins): the full guide, which says what to supply, what runs, what comes back and what it costs. With template (openmm-s

  • campaign_request

    No key needed. Sends a study request to a person and returns a request_id, the tier that fits, the price band, what we still need from you, the next step and the claim scope. Nothing is run or charged. Call it after reading a guide.

  • account

    Balance in USD (`balance_usd`), the part of it held by your unfinished jobs (`reserved_usd`, `held_jobs`) and what a new submit can use (`available_usd` = balance - reserved; a submit needs wall_limit_s x rate of it), how jobs are priced (`pricing.mode` job = the deck's own work

  • submit_job

    One call: create a hosted GPU job, upload the deck given INLINE as {relative_path: text}, and queue it. Total inline size <= 8 MB; for larger decks use create_job, PUT the tarball to upload_url, then start_job. Billing starts at the first heartbeat (state running) and stops at do

  • create_job

    Step 1 of the two-step path for big decks: validates the spec, reserves a job id, returns a presigned upload_url. PUT the deck as a .tar.gz (<= 2 GB, relative paths, input at `input`) to upload_url, then call start_job.

  • start_job

    Step 2: queue a job whose deck tarball has been uploaded. A GPU pod is launched; billing starts when it reports running.

  • job_status

    State (created|uploaded|queued|launching|running|uploading|done|failed|cancelled), GPU, rate, billed seconds, cost so far, exit code, error, last thermo lines, and the post-run verdict: clean | warnings (a done run whose log shows QEq CG failures, lost atoms, a fix halt, or no en

  • job_log

    The last <= 20 thermo/log lines the running pod reported (30 s heartbeat). Full log.lammps is in the results tarball.

  • job_results

    For a done/failed job: a presigned download_url (valid ~7 days) for the results tarball (work/, log.lammps, exitcode). Results are deleted 30 days after the run.

  • list_jobs

    Jobs of this API key, newest first. Compact rows (id, state, label, created, finished, gpu, cost_usd, error) unless full=true.

  • delete_results

    For a finished job: delete its deck and results tarballs immediately instead of at the automatic 30-day purge. Metadata and billing records are kept; results can no longer be downloaded.

  • capabilities

    Capability manifest of the hosted runners: LAMMPS version, installed packages, and every style by category with gpu=true (KOKKOS-accelerated) or gpu=false (exists, but runs on the pod's CPU cores at the GPU rate). Default = compact summary; runner=lammps&full=true returns the who

  • preflight_deck

    Dry run of the check submit_job performs: which styles the deck asks for are MISSING on every hosted LAMMPS image (the run would exit at startup), which are CPU-only, whether its pair style will use the GPU at all, and which image (`runner`) the job will be routed to — decks need

  • cancel_job

    Cancel a job that is not finished. A running job is billed up to the cancel time; its pod is terminated.

Security scan

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Recent checks

WhenResultHTTPTime
4 h agoPassed200144 ms