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

Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds transparency by explaining the output components (score, trend, z_score, 20-year baseline composites), the data source, and the update frequency ('Updates: weekly'). This adds behavioral context beyond the annotations.

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 and front-loaded: the first sentence immediately states the output and metric definition, and the second covers use cases and update cadence. Every sentence adds value, with no redundancy or 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?

The tool has no output schema, so the description appropriately lists the main output fields (score, trend, z_score, baseline composites). It also provides update frequency, data sources, and use cases. It lacks a brief note on interpreting higher/lower scores, but overall it is sufficiently complete for an agent to select and invoke the tool 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?

Schema description coverage is 100% and the single 'days' parameter is fully described in the schema, including its optional nature, meaning (return history series), constraints, and access-tier requirement (Gold tier). The main description adds no parameter-specific semantics, but the schema already covers it, so the baseline of 3 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 ('Returns') and clearly identifies the resource: a 0-100 construction labor-cost divergence score, including how it is calculated (spread between YoY construction and manufacturing average-hourly-earnings growth) and the data series used (BLS CES CES2000000003 vs CES3000000003). This level of specificity distinguishes it from generic sibling tools and fully explains what the tool does.

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 explicit use cases: 'Call when the user asks about construction wage pressure, building-trades labor scarcity, or homebuilder margin risk, or when timing bid escalation contingencies, subcontractor rate locks, or housing-starts exposure.' This is clear contextual guidance, though it does not mention when not to use the tool or name alternatives.

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