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

With no annotations, the description carries the full burden. It discloses key behaviors: monitoring 8 regions, returning SMI score with regional breakdown and 7-day anomalies, and using weather normalization to isolate industrial demand. This goes beyond basic expectations, though it does not mention limitations, error handling, or data freshness.

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 5 sentences, each earning its place: purpose, regional coverage, output, methodology, and use case. It is comprehensive but not verbose. The front-loaded first sentence immediately conveys the core function.

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?

With no output schema and no annotations, the description is remarkably complete. It explains what data is returned, the geographic scope, the time window, the ranking by severity, and the underlying methodology (SMI). It provides enough information for an agent to invoke the tool correctly and interpret results, making it fully complete for its complexity.

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 input schema is empty, so there is nothing to explain. The description adds value by detailing what the tool returns, which compensates for the lack of parameters. The baseline of 4 for zero-parameter tools applies, and the description does not introduce confusion.

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 verb (detect) and resource (unusual electricity demand patterns signaling manufacturing disruptions), with specific scope (8 US power grid regions) and output (SMI score, regional breakdown, past 7 days anomalies). It distinguishes itself from sibling tools like get_energy_forecast by focusing on manufacturing anomaly detection with a unique index.

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 by identifying target users (commodity traders, procurement teams) and the benefit (early manufacturing signals, supply change anticipation). It does not explicitly name alternatives or define when not to use, but the context is strong enough to guide selection.

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