MDEngine

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

我該用這個嗎

品質與安全性

A
說明品質
98%
結構描述完整度
93%
命名品質
90%
汙染風險
100%
權限相符程度
100%
協定合規性
100%

發現項目(1)

  • LOWTool 'account' description lacks action verb在 account 中

根據工具定義與協定合規性的自動化分析。

上下文成本

~2,068Token(工具定義)
~865 B典型回應大小
中等的注意力影響(128k 上下文的 1.62%)

這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高,可用於其他工作的注意力就越少。

安裝

一鍵安裝

將以下內容加入你的 `claude_desktop_config.json` 檔案:

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

遠端端點

https://api.forcefieldsilicon.com/mcpstreamable-http

它能做什麼

工具清單

工具(12)

🟢 唯讀🟡 寫入🔴 刪除⚪ 未知
🟢account

Balance in USD, how jobs are priced (`pricing.mode` job = the deck's own work in atom-steps at per-class prices, capped at wall_limit_s x rate; metered = per second of pod time), the rate table, and the key id of the API key in use.

輸入結構描述

{
  "type": "object",
  "properties": {}
}
🟡submit_job(input, label, gpu, wall_limit_s, estimate_s, ...)

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 done/failed/cancelled. The deck is executed as a program on an isolated GPU pod.

輸入結構描述

{
  "type": "object",
  "properties": {
    "input": {
      "type": "string",
      "description": "Relative path of the LAMMPS (or runner) input script inside the deck, e.g. in.lmp"
    },
    "label": {
      "type": "string",
      "description": "Free text <= 120 chars shown in job lists"
    },
    "gpu": {
      "type": "string",
      "description": "GPU class; call account for the offered classes and how jobs are priced. Default any = cheapest available"
    },
    "wall_limit_s": {
      "type": "integer",
      "description": "Hard cap in seconds (default 14400, max 86400); the job fails at the cap and is billed to it"
    },
    "estimate_s": {
      "type": "integer",
      "description": "Your runtime guess in seconds; only used for the balance pre-check (min 900 s at the rate)"
    },
    "runner": {
      "type": "string",
      "description": "Runner flavour: lammps (default) or openmm (beta)"
    },
    "launch": {
      "type": "string",
      "description": "Launch template; omit for the default KOKKOS/CUDA LAMMPS command line"
    },
    "files": {
      "type": "object",
      "additionalProperties": {
        "type": "string"
      },
      "description": "Deck contents: {\"in.lmp\": \"...\", \"data.al\": \"...\"}; paths relative, no '..'; must include `input`"
    }
  },
  "required": [
    "input",
    "files"
  ]
}
🟡create_job(input, label, gpu, wall_limit_s, estimate_s, ...)

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.

輸入結構描述

{
  "type": "object",
  "properties": {
    "input": {
      "type": "string",
      "description": "Relative path of the LAMMPS (or runner) input script inside the deck, e.g. in.lmp"
    },
    "label": {
      "type": "string",
      "description": "Free text <= 120 chars shown in job lists"
    },
    "gpu": {
      "type": "string",
      "description": "GPU class; call account for the offered classes and how jobs are priced. Default any = cheapest available"
    },
    "wall_limit_s": {
      "type": "integer",
      "description": "Hard cap in seconds (default 14400, max 86400); the job fails at the cap and is billed to it"
    },
    "estimate_s": {
      "type": "integer",
      "description": "Your runtime guess in seconds; only used for the balance pre-check (min 900 s at the rate)"
    },
    "runner": {
      "type": "string",
      "description": "Runner flavour: lammps (default) or openmm (beta)"
    },
    "launch": {
      "type": "string",
      "description": "Launch template; omit for the default KOKKOS/CUDA LAMMPS command line"
    }
  },
  "required": [
    "input"
  ]
}
⚪start_job(id)

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

輸入結構描述

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Job id, e.g. MDJOB-20260907-3F200C"
    }
  },
  "required": [
    "id"
  ]
}
🟢job_status(id)

State (created|uploaded|queued|launching|running|uploading|done|failed|cancelled), GPU, rate, billed seconds, cost so far, exit code, error, last thermo lines.

輸入結構描述

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Job id, e.g. MDJOB-20260907-3F200C"
    }
  },
  "required": [
    "id"
  ]
}
🟢job_log(id)

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

輸入結構描述

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Job id, e.g. MDJOB-20260907-3F200C"
    }
  },
  "required": [
    "id"
  ]
}
🟢job_results(id)

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.

輸入結構描述

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Job id, e.g. MDJOB-20260907-3F200C"
    }
  },
  "required": [
    "id"
  ]
}
🟢list_jobs(limit, full)

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

輸入結構描述

{
  "type": "object",
  "properties": {
    "limit": {
      "type": "integer",
      "description": "1..200, default 20"
    },
    "full": {
      "type": "boolean",
      "description": "Return the complete status object per job (default false)"
    }
  }
}
🔴delete_results(id)

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.

輸入結構描述

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Job id, e.g. MDJOB-20260907-3F200C"
    }
  },
  "required": [
    "id"
  ]
}
🟢capabilities(runner, full)

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 whole style table. Use preflight_deck to check a specific deck.

輸入結構描述

{
  "type": "object",
  "properties": {
    "runner": {
      "type": "string",
      "description": "lammps | openmm; omit for all"
    },
    "full": {
      "type": "boolean",
      "description": "Include the full style table (large)"
    }
  }
}
🟢preflight_deck(input, files)

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 needing packages beyond the fast default image run on lammps-full automatically. Nothing is created or billed.

輸入結構描述

{
  "type": "object",
  "properties": {
    "input": {
      "type": "string",
      "description": "Relative path of the LAMMPS (or runner) input script inside the deck, e.g. in.lmp"
    },
    "files": {
      "type": "object",
      "additionalProperties": {
        "type": "string"
      },
      "description": "Deck contents {path: text}, must include `input`"
    }
  },
  "required": [
    "input",
    "files"
  ]
}
🔴cancel_job(id)

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

輸入結構描述

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Job id, e.g. MDJOB-20260907-3F200C"
    }
  },
  "required": [
    "id"
  ]
}

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