Rack Density Analyzer

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description doesn't need to restate those. It adds value by detailing what the tool returns: density classification, cooling strategy, airflow estimates, and thresholds for containment and liquid cooling. This gives the agent a clear picture of the tool's behavior and output beyond the annotation hints.

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 description is well-structured with a concise intro, a clear Args list, a Returns line, and illustrative examples. Every sentence serves a purpose, and the overall length is appropriate for the tool's complexity. No filler or redundancy.

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 fully covers what the tool does, the parameters it accepts, and the outputs it produces. With no output schema, the description carries the burden of explaining return values, and it does so clearly (classification, strategy, airflow, recommendations). Examples add practical context, making it complete for an analysis tool.

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?

The input schema has 100% parameter coverage with detailed descriptions, so the baseline is 3. The description goes beyond the schema by providing concrete value examples (e.g., 8 kW typical enterprise, 40+ GPU) and natural-language mapping examples, which adds practical meaning for parameter selection.

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 starts with a specific verb ('Analyze') and resource ('rack density') and clearly states the tool's purpose: to classify density and recommend cooling strategies. This distinguishes it from siblings like dc_calculate_cooling_load or dc_gpu_cooling_optimizer, which focus on different aspects of cooling.

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?

The description provides clear context on when to use the tool through examples and a returns summary, but it doesn't explicitly state when not to use it or point to sibling alternatives. This is a strong but not explicit usage guide.

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