job_energy_footprint

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

Beyond the readOnlyHint annotation, it discloses a critical behavioral caveat: the output is a simulation, not a measurement, because ROMEO runs no SLURM energy-accounting plugin. It also explains exactly when uncertainty drops (running job → real GPU power sampled), which is precisely the kind of epistemic framing an agent needs before trusting the numbers.

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?

Three sentences, front-loaded with the core purpose and then the accuracy caveat. The capitalized ATTENTION warning is deliberate and earns its prominence because it prevents misuse of the result.

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?

An output schema exists, so return-value explanation is unnecessary, and the description covers the accuracy model and the running-job nuance well. The one real gap is the unexplained gpu_load_factor parameter, which an agent must guess at.

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% and the description never mentions either parameter. In particular, gpu_load_factor (default 0.6) is a significant modeling knob whose meaning and effect on the estimate are left entirely undocumented, so the description fails to compensate for the schema gap.

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 names a specific verb and resource in its first clause ('estimates the energy consumed and carbon footprint of a job'), which is immediately distinguishable from adjacent tools like job_efficiency, profile_job or job_live_metrics. An agent can identify the tool's domain without opening the schema.

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 clear usage context: results are a model when SLURM energy accounting is unavailable, and accuracy improves substantially for in-progress jobs where GPU power is actually sampled. It does not, however, name an alternative tool or state exclusions (e.g. when to prefer job_efficiency instead).

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