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

With no annotations, the description fully details the tool's behavior: it monitors 8 power grid regions, detects sudden drops/surges, returns the SMI score with regional breakdown and anomalies from the past 7 days ranked by severity. It also explains the SMI's weather normalization methodology, providing rich behavioral context beyond the tool's name.

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 long, with the core purpose stated first, followed by monitoring details and output, and finally the user context. Every sentence contributes value, and the structure is logical and 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 covers the return values (current SMI score, regional breakdown, anomalies from past 7 days), the monitored regions, and the methodology. While it does not specify an exact data format, the lack of an output schema is compensated by a clear high-level overview. It could be slightly more precise about output structure or thresholds, but it is adequate for a no-parameter 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 input schema has 0 parameters, so the baseline is 4. The description correctly omits parameter details since there are none to explain. It does not need to add anything beyond the schema.

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's function: detecting unusual electricity demand patterns that signal manufacturing disruptions. It explicitly names the monitored regions and describes the output (SMI score, regional breakdown, anomalies). This differentiates it from sibling tools like get_energy_forecast or get_predictive_signals.

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 context for use: it is used by commodity traders for early manufacturing signals and by procurement teams to anticipate supply changes. It implies when to use the tool (when early manufacturing signals or supply change anticipation is needed) but does not explicitly mention when not to use it or name alternative tools.

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