Size an AI data center

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

No annotations are provided, so the description carries the full behavioral burden. It says the tool generates a design basis, but it does not disclose whether it is read-only, whether it persists anything, what permissions are needed, or how errors are handled. The mention of 'profile-aware' is too vague to count as meaningful behavioral disclosure.

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?

It is a single, front-loaded sentence with no redundant or filler language. Every part of the sentence contributes to identifying the tool's output and input context.

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 tool has 9 parameters, 5 required, nested objects, no annotations, and no output schema, yet the description only gives a one-line summary. It does not explain the output structure, behavioral expectations, or how the optional inputs affect the result, leaving substantial gaps for a complex sizing 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?

Schema description coverage is 100%, so the input schema already provides per-parameter meaning. The description adds only a high-level grouping ('OPR inputs and optional project data') and no parameter-level detail beyond what the schema states, so the baseline of 3 is appropriate.

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 states a specific verb ('Generate') and resource ('AI data center design basis'), with the input source ('OPR inputs and optional project data'). It is clear what the tool produces, but it does not distinguish itself from the sibling tools 'validate' and 'layout'.

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?

There is no explicit guidance on when to use this tool versus alternatives. The phrase 'from OPR inputs and optional project data' hints at context, but it does not state prerequisites, exclusions, or when a caller should prefer 'validate' or 'layout'.

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