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

With no annotations, the description carries the behavioral burden and does add useful context by naming three data series and noting that chip counts appear on a separate series. However, it does not disclose whether operator/metric are optional, what the default returned record looks like, or any response-format details beyond the series names.

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 compact: two sentences with no filler. The core subject ('Comparable AI data-center capacity') is front-loaded, and every clause contributes either a metric detail or a filtering instruction. It avoids repeating schema examples and stays focused.

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 only two optional parameters and no output schema, the description supplies the essential context: what data is returned (power, H100-equivalents, chips) and the operator filter. The schema fills in the remaining parameter semantics, such as the 'full operator record is always returned' behavior, so an agent can select and invoke the tool without major gaps.

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% and both parameters already have descriptions, so the baseline is 3. The tool description adds genuine meaning by mapping the metric enum values to concrete concepts (power→MW, compute→H100-equivalents, chips→raw chip counts) and explaining the filtering behavior via operator slug, going beyond the schema's generic parameter 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 clearly identifies the resource as AI data-center capacity and enumerates concrete metrics (IT power in MW, H100-equivalents, raw chip counts), which readily distinguishes it from sibling tools like get_robotaxi_tracker and get_company. However, the main clause is a noun phrase ('Comparable AI data-center capacity') rather than an explicit action verb such as 'returns' or 'retrieves', so purpose is clear but not maximally explicit.

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 implies use when an agent needs comparable AI data-center capacity, and it explains that filtering by operator slug is supported. It does not explicitly state when to prefer this tool over alternatives like search_companies or get_company, nor does it mention any exclusions or when-not-to-use cases.

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