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

With no annotations, the description carries the full burden. It discloses that it returns both historical actuals and projections, and explains the isActual flag that distinguishes them. It also explicitly states the data content (crude oil, natural gas, electricity, renewables, petroleum). Missing are details like update frequency or data ordering, but the key behavioral aspects are covered.

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 three sentences, front-loaded with the primary purpose. The sentence about being 'the most widely referenced energy forecast' adds credibility but is somewhat promotional. The final sentence about users is helpful for context. Overall, every sentence has purpose with minimal fluff.

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 fairly complete. It explains the data source, the types of data, the distinction between actuals and forecasts, and the target audience. It lacks specifics like the forecast time horizon (e.g., 18 months) or update cadence, but these are minor given the STEO is a known product.

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 input schema has zero parameters, so the baseline is 4. The description does not need to explain parameter semantics, but it provides valuable context about what data is returned, which indirectly explains that the tool requires no inputs and returns a fixed dataset.

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 a well-defined resource ('US Energy Information Administration's Short-Term Energy Outlook'), listing detailed contents (crude oil prices, natural gas prices, electricity generation, etc.). It clearly distinguishes this from siblings like commodity_price_monitor or get_energy_breakdown by emphasizing the forecast nature and the isActual flag.

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 states the tool returns official government forecasts and historical actuals, giving clear context for when to use it (e.g., for energy outlook data). It mentions target users (energy traders, logistics companies, macro analysts), which implies use cases. However, it does not explicitly compare alternatives or say when not to use it, but the content is sufficient for basic guidance.

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