get_manufacturing_anomalies

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

No annotations are present, so the description carries full burden. It discloses monitoring 8 regions, 7-day anomaly window, and weather normalization, but lacks details on data freshness, update frequency, or any limitations. Adequate but not thorough.

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?

Five sentences with front-loaded purpose. Information-dense but clear. Could be slightly more structured with bullet points, but still efficient.

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?

Given no parameters and no output schema, the description fully covers what the tool does, its inputs, outputs, and use cases. No critical information is missing for an agent to select and invoke it correctly.

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?

There are zero parameters, and schema coverage is 100%. The description adds value by explaining what the output includes (SMI score, regional breakdown, anomalies), which compensates for no params. Baseline 4 is appropriate.

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 ('detect') and resource ('manufacturing disruptions via electricity demand anomalies'). It clearly distinguishes from sibling tools like 'get_port_congestion_trends' by focusing on manufacturing signals from power grid data across 8 US regions.

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?

States the tool provides early signals before official reports and targets commodity traders and procurement teams, giving clear context. However, it does not explicitly state when not to use it or name alternative tools for similar purposes.

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