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CodeMonk6

RISBridge MCP

by CodeMonk6

Run a script (auto-setup)

ris_run

Submits scripts as Slurm jobs on WashU RIS Compute2, auto-creating project and PyTorch/TensorFlow environments if missing—no manual conda; supports GPU/CPU and dry-run previews.

Instructions

One call: ensures your project + environment exist (auto-builds a PyTorch/TF env if missing) and submits your script — GPU or CPU. No manual conda steps. dryRun preview by default; re-call with dryRun=false, confirm=true to run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dryRunNo
scriptYes
accountNo
confirmNo
envNameNotorch
profileNoNamed profile from ~/.risbridge-mcp/config.json. Omit for the default.
projectYesProject name; becomes one directory under the storage workspace.
gpuCountNo
needsGpuNo
walltimeNo01:00:00
frameworkNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already flag it as a non-read-only, open-world, non-destructive operation. The description adds real value beyond that: it discloses that the tool auto-builds a missing PyTorch/TF environment and that execution is gated behind dryRun=false plus confirm=true, so an agent understands the mutation actually requires an explicit second call.

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

Conciseness4/5

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

Front-loads the core action ('One call: ensures...') and the safety gate, with no filler sentences. The em-dash-heavy packing is dense but each clause conveys distinct information.

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

Completeness3/5

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

For an 11-parameter, low-coverage, no-output-schema orchestration tool, the description captures the high-level flow and the critical confirm gate but omits meanings for several resource-shaping parameters (walltime, gpuCount, account, envName). Adequate but with clear gaps.

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

Parameters3/5

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

Schema coverage is only 18%, so the description must carry param meaning, and it only partially does. It clarifies script submission, dryRun/confirm gating, GPU-vs-CPU (needsGpu), and framework selection (PyTorch/TF), but leaves account, envName, gpuCount, walltime, and the profile/account relationship undocumented.

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

Purpose4/5

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

States a specific verb+resource ('run a script') and clarifies scope by describing the orchestration: ensures project+env exist, auto-builds the env, then submits. This distinguishes it from the narrower ris_submit_* siblings as an all-in-one path, though it never explicitly names an alternative.

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

Usage Guidelines3/5

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

Gives clear operational guidance on the two-step gate ('dryRun preview by default; re-call with dryRun=false, confirm=true to run'), which is genuinely useful. However, it never states when to prefer this over ris_plan_run, ris_ensure_env, or the specific ris_submit_* job tools, so its role as an alternative is only implied.

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