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

With no annotations, the description carries the burden. It discloses what the tool returns (a comprehensive list of data) but does not mention behavioral traits such as data freshness, update cadence, sourcing, or limitations. It accurately describes the output but lacks deeper behavioral context that an agent might need to assess reliability or side effects.

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 longer than necessary but well-structured, beginning with the main purpose and then enumerating the data components. Each sentence provides relevant value, and the audience sentence is helpful context. It could be slightly trimmed, but it is not verbose or redundant.

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 tool with no parameters and no output schema, the description is highly complete: it explains the purpose, lists all returned data categories, and indicates target users. It lacks details on data source or refresh frequency, but given the context, it provides enough information for an agent to understand what to expect when invoking the 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?

The tool has 0 parameters and the schema is empty (100% coverage), so the baseline is 4. The description compensates by explaining exactly what data is returned, which is essential for a parameterless tool. It adds value by detailing the types of data included, making the output behavior clear without needing parameter documentation.

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 states the tool returns comprehensive US energy market status, with a specific verb and resource. It lists the data components (crude oil, natural gas, retail fuel, storage, refinery utilization, stocks, import/export flows), which establishes its purpose. It references the GDI Energy pillar and the disaggregated view, but does not explicitly contrast with siblings like get_energy_forecast, so it lacks direct sibling differentiation.

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 gives context for usage: 'for supply chain cost analysis' and identifies target users (supply chain cost analysts, transportation managers, energy procurement teams). This implies when to use the tool but does not explicitly state when to choose it over alternatives or provide any exclusions. It is clear context but lacks direct guidance on alternatives.

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