gpu_submit_job

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

With no annotations, the description carries the full behavioral-disclosure burden and does a strong job on idempotency, deduplication, ambiguous submits not being resubmitted, account pinning, quota exhaustion not rotating accounts, premium guards, and `dry_run` not submitting. It still leaves some gaps around failure modes, authorization requirements, and side effects, and it references `mode` and `allow_real_jobs` even though those are not in the input schema.

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

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

The purpose is front-loaded in the first sentence and most of the dense content is relevant to a complex 12-parameter submission tool. The single-paragraph format is less scannable than it could be, but there is little outright fluff.

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

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

The description is behaviorally rich for a tool with no annotations and 0% schema description coverage, and the output schema means return values need not be explained in detail. However, it does not fully document the 12 input parameters and introduces parameters (`mode`, `allow_real_jobs`) that are absent from the schema, leaving an agent with ambiguity about what can actually be supplied.

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

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

Schema description coverage is 0%, so the description must compensate for all 12 parameters. It adds meaningful semantics for `provider`, `high_mem`, `timeout_seconds`, `idempotency_key`, `account`, `dry_run`, and partially `allow_premium`, but it leaves `name`, `gpu_type`, `gpu_count`, `script_path`, and `min_gpu_memory_gb_per_gpu` unexplained while also mentioning schema-absent fields.

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

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

The description opens with a specific verb and resource: "Submit a script." That is clear enough for an agent to understand the core operation, but it never distinguishes this tool from the sibling `l2kc_submit_job` or explicitly scopes it as the GPU submission tool.

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

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

It gives useful conditional guidance around `dry_run`, premium GPUs, Colab timeouts, and real jobs, which implies when certain options apply. However, it never states when to choose this tool over `l2kc_submit_job`, `gpu_recommend_backend`, or other siblings, and it provides no explicit when-not-to-use guidance.

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