Data Center Reference Data

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds 'Returns reference data tables' and clarifies the tier filter, providing some context beyond annotations. No additional behavioral details like rate limits or error handling are included, but none are critical for this read-only tool.

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 purpose, Args, Returns, and Examples sections. The Args section somewhat repeats the schema, but the examples justify the inclusion. It is concise enough for the tool's complexity, with no unnecessary filler.

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?

For a simple reference lookup with fully described parameters and safety annotations, the description provides sufficient detail: categories, optional tier filter, return type, and usage examples. Lack of an output schema is acceptable, though more specifics about table format would improve completeness.

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 coverage is 100% with descriptions and enums for both parameters. The description goes further by mapping natural language examples to argument values (e.g., 'What are the Tier III requirements?' -> category=tier_requirements, tier=3), enhancing an agent's understanding of how to construct valid calls.

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 uses a clear verb ('look up') and specifies the resource ('data center engineering reference data'), listing four distinct categories. It distinguishes itself from sibling analysis tools by focusing on reference data retrieval, but does not explicitly disambiguate from overlapping tools like dc_assess_tier_classification.

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?

Examples show common query patterns, but there is no explicit guidance on when to use this tool versus the sibling analysis tools (dc_calculate_*, dc_analyze_*). The context of 'reference data' implies use for static lookups, yet exclusions are not stated.

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