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Glama

MDEngine

by com.forcefieldsilicon

Submit a deck (inline files) and start it

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gpuNoGPU class; call account for the currently offered classes and hourly rates. Default any = cheapest available
filesYesDeck contents: {"in.lmp": "...", "data.al": "..."}; paths relative, no '..'; must include `input`
inputYesRelative path of the LAMMPS (or runner) input script inside the deck, e.g. in.lmp
labelNoFree text <= 120 chars shown in job lists
launchNoLaunch template; omit for the default KOKKOS/CUDA LAMMPS command line
runnerNoRunner flavour: lammps (default) or openmm (beta)
estimate_sNoYour runtime guess in seconds; only used for the balance pre-check (min 900 s at the rate)
wall_limit_sNoHard cap in seconds (default 14400, max 86400); the job fails at the cap and is billed to it

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • changedInput schema / properties / gpu / description
      Previous value: -"GPU class: any (cheapest available, default), rtx4090, a100 -- see account.rate_table"New value: +"GPU class; call account for the currently offered classes and hourly rates. Default any = cheapest available"
    • changedInput schema / properties / runner / description
      Previous value: -"Runner flavour: lammps (default) or openmm"New value: +"Runner flavour: lammps (default) or openmm (beta)"
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that the tool creates and queues a job, mentions billing start/stop conditions, and notes execution on an isolated GPU pod. This adds context beyond the annotations (readOnlyHint false, destructiveHint false) without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, focused paragraph that front-loads the main function, then covers limits, alternatives, and billing. No redundant wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides enough context for an agent to decide when to call it and what to expect: the inline upload limit, the alternative for larger decks, and billing semantics. No output schema exists, so no return details are needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers all parameters with detailed descriptions. The description adds a critical constraint (total inline size ≤8 MB) and clarifies the relationship between input and files, enhancing parameter understanding slightly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: creating and submitting a hosted GPU job with inline files in one call. It distinguishes from create_job and start_job for larger decks, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly instructs when to use this tool (inline files ≤8 MB) and when to use the alternative create_job/start_job workflow. Billing behavior is also explained, providing clear usage conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.3/5.0
Disambiguation5/5

Each tool serves a distinct purpose: account and capabilities cover account/configuration info, while create_job, start_job, and submit_job represent clearly differentiated job submission stages. Monitoring, results, cancellation, and deletion tools are all non-overlapping.

Naming Consistency4/5

Tool names mostly follow a clear snake_case verb_noun pattern (cancel_job, create_job, delete_results, list_jobs, start_job, submit_job). A few noun-only names like account, capabilities, job_status, and job_results deviate slightly, but the pattern remains recognizable and predictable.

Tool Count5/5

With 12 tools, the set covers the full job lifecycle without being bloated. Each tool maps to a meaningful operation, and the count is well within the typical 3-15 range for a domain-specific server.

Completeness5/5

The tool surface covers the necessary workflow: checking account/capabilities, submitting and starting jobs, monitoring status/logs, retrieving results, canceling jobs, and deleting results. No essential operation for the stated GPU job management domain appears missing.

Resources