Commissioning Plan Generator

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

Beyond the readOnly/idempotent annotations, the description discloses meaningful behavioral details: it follows ASHRAE levels, scales to facility size and tier complexity, and returns a structured plan with phases, test procedures, durations, milestones, and recommendations. It also explains the meaning of each commissioning level, providing transparency about what the generated plan contains.

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 and front-loaded with the core purpose. It includes the level definitions, an Args section, and examples. The Args section partially duplicates the schema descriptions, but the overall organization is logical and each section serves a clear role. It is longer than minimal but appropriate for a complex generation tool.

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?

Given there is no output schema, the description appropriately explains the return value ('structured plan with phases, test procedures, durations, milestones, and recommendations'). It also defines the commissioning levels, parameter semantics, and provides examples. This is a complete, self-contained description for an agent to select and invoke the tool correctly.

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 already covers 100% of the parameters with descriptions, so the baseline is 3. The description adds value through the commissioning-level definitions and two realistic examples that show how to interpret user phrasing into exact parameter values (e.g., '5 MW Tier III' -> facility_size_kw: 5000, tier_level: 3). This practical mapping goes beyond the schema's static field descriptions.

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: 'Generate a comprehensive data center commissioning plan following ASHRAE guidelines (Levels 1-5).' This clearly distinguishes it from sibling tools like dc_calculate_cooling_load or dc_assess_tier_classification. It also outlines the structured output and scaling behavior, making the tool's purpose unmistakable.

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 by explaining what the plan includes and how it scales, and it supplies concrete examples mapping natural-language requests to parameter values. It does not explicitly state when not to use this tool or name alternatives, but none of the sibling tools overlap with commissioning-plan generation, so the usage context is sufficiently clear.

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