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

With no annotations, the description carries the full burden of behavioral disclosure. It describes what data is returned but does not explicitly state read-only behavior, side effects, or limitations. The 'Get' prefix implies a safe read operation, but it is not stated.

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 structured with a clear main purpose, a detailed list of return data, and contextual framing. Every sentence contributes information without redundancy, and the most important information is front-loaded.

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 comprehensively lists the data categories and provides context about its use in supply chain analysis. It lacks explicit units or freshness information, but with no parameters and standard energy metrics, it is sufficiently complete for an agent to understand what it will receive.

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 tool has zero parameters, so the baseline is 4. The description does not discuss parameters because there are none, and the schema coverage is trivially 100%.

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 specific verb ('Get') and resource ('comprehensive US energy market status') and clearly lists the data components, distinguishing it from siblings like get_energy_forecast or risk_pillar_breakdown by framing it as the 'disaggregated view' instead of a single risk number.

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 usage context ('for supply chain cost analysis') and mentions target users, but it does not explicitly state when not to use it or name alternative tools. The 'instead of a single risk number' hint partially addresses this.

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