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NOVTRIQ Engineering

thermal_load

Estimate data centre thermal load and cooling requirements. Returns heat load, cooling capacity, airflow, chiller size, PUE, power density, and cooling system comparison.

Instructions

Thermal Load Estimator for data centres / critical facilities. Returns total heat load, cooling capacity, units, airflow, chiller size, PUE estimate, power density, and a CRAC/CRAH/DLC/immersion comparison.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
it_load_kwYes
redundancyYes
lighting_kwNodefault 5
cooling_typeYes
room_area_m2Yes
ambient_temp_cYes
ups_efficiency_pctYes1-99.9
Behavior2/5

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

With no annotations, the description carries the full burden of disclosing behavioral traits. It lists outputs but does not explain the calculation methodology, assumptions, potential limitations, or whether this is a read-only estimation. The absence of any side-effect or prerequisite information leaves the agent without crucial context for a complex estimation tool.

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?

The description is a single sentence that efficiently conveys the tool's purpose and output list. It is not overly verbose, but it crams many outputs into one sentence, which slightly reduces scannability. Still, it earns a 4 for being compact and information-dense.

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

Completeness2/5

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

This tool has 6 required parameters, 2 enums, low schema coverage, no output schema, and no annotations. The description provides only a high-level output list, omitting parameter explanations, usage context, and behavioral caveats. It is insufficient for an agent to invoke the tool correctly without additional information.

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

Parameters1/5

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

Schema description coverage is only 29%, and the description does not explain any of the 7 parameters. It only lists outputs, not inputs, so the agent gets no additional meaning about parameters like 'it_load_kw', 'ups_efficiency_pct', or 'cooling_type'. The tool relies heavily on the schema, which itself provides minimal descriptions.

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?

The description clearly states the tool is a 'Thermal Load Estimator' and enumerates specific outputs (heat load, cooling capacity, etc.), which conveys a concrete function. However, it does not explicitly differentiate from siblings like 'estimate_cooling_load' or 'calculate_pue', so it earns a 4 rather than 5.

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

Usage Guidelines2/5

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

No explicit guidance is given for when to use this tool versus alternatives such as 'estimate_cooling_load' or 'calculate_pue'. The mention of 'data centres / critical facilities' implies a context, but there is no when-to-use, when-not-to-use, or alternative recommendation.

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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