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

With no annotations, the description carries the full burden. It discloses the methodology (weather-normalized electricity demand), the inverted scale (lower = stronger), and specific thresholds (0-35 STRONG, etc.). It also reveals the 24-hour lead time. It doesn't mention data freshness or failure modes, but the detail provided is substantial.

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 a single paragraph with multiple details, but every sentence adds value: the lead time, the methodology, the regions, the outputs, the scale, and the target audience. It is a bit long, but not bloated, and the core purpose is 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?

Given the complexity and lack of output schema, the description is quite complete: it tells the agent what the tool does, the data source, the regional breakdown, the scale interpretation, and the intended audience. It could add update frequency or confidence intervals, but for tool selection and invocation, it is sufficient.

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, so the baseline is 4. The description exceeds this by explaining what the tool returns in detail: regional and national scores, trend direction, and comparison to INDPRO. It defines the scale and interpretation, giving full meaning beyond the empty 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 opens with a specific verb+resource: 'Detect US manufacturing output changes up to 24 hours before official government reports.' It clearly differentiates from sibling tools by naming the patent-pending SMI, the 8 grid regions, and the output (scores, trend, INDPRO comparison). The purpose is immediately unambiguous.

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 ('Used by commodity traders, economic analysts, and hedge funds as a leading manufacturing indicator'), implying early insight use case. However, it does not explicitly state when NOT to use it or how it differs from alternatives like get_manufacturing_anomalies, so it stops short of a 5.

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