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

Annotations already declare readOnlyHint=true, so the description's additional context about score range, component field names, underlying data sources (BLS PPI, FRED), daily update cadence, and historical depth adds meaningful behavioral insight. It does not contradict annotations and covers the tool's behavior well beyond what structured metadata provides.

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 compact yet dense with useful information: what it returns, data sources, component names, and explicit usage scenarios. Every sentence serves a purpose, and the text is front-loaded with the core definition followed by directives and update frequency, with no filler.

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, the description compensates by listing all expected return fields (cotton_sub_score, cotton_ppi_yoy_pct, etc.) and specifying data sources, update frequency, and historical depth. It clearly communicates the score's interpretation (cost pressure, higher score implies higher pressure) and intended use cases, making it complete for an agent.

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 only parameter, 'days', is fully described in the schema with 100% coverage, so the description does not need to add more. The description does not mention the parameter, but the schema adequately documents its purpose and behavior, meeting the baseline score.

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 ('Returns') and identifies a unique resource ('textile and apparel input cost-pressure score') with details on data sources and components. This clearly distinguishes it from the numerous other adw.* tools, making the purpose instantly recognizable.

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 explicitly lists several scenarios to call the tool: 'cotton prices, fabric or apparel input inflation, textile margins', and timing for purchase orders, supplier negotiations, or retail price changes. It does not mention exclusions or alternative tools, but the guidance is sufficiently clear for an agent to decide when to invoke it.

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