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

Annotations already declare readOnlyHint=true, and the description adds meaningful behavioral context: the score interpretation ('above 50 = accelerating'), data frequency ('monthly since 1996'), update cadence ('Updates: monthly'), and output components (top_drivers, source_lineage). It does not disclose hidden behaviors like rate limits or access constraints, but the read-only nature is covered by annotations and the description enriches the mental model.

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 information-dense but concise, front-loading the core score definition and calculation, then covering output fields, use cases, and update frequency in a tight sequence. Every sentence contributes value, and there is no filler or repetition.

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 tool's moderate complexity, the description sufficiently explains what the tool returns, how the score is composed, what the output fields include, when to use it, and its temporal coverage. The optional parameter is fully described in the schema, and the read-only behavior is declared via annotations. No critical context appears missing for an agent to select and invoke this 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%, so the single optional 'days' parameter is fully documented in the schema. The tool description adds no additional explanation of this parameter, relying on the schema to carry the semantic weight. Per the rubric, this is a baseline 3: adequate but not enhanced by the description.

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') with a concrete resource ('0-100 US infrastructure investment velocity score') and details the underlying data and computation (Census construction value-in-place via FRED, equal-weight total and public spend, YoY plus 3-month momentum). It clearly distinguishes itself from the many generic sibling tools by describing unique output fields like top_drivers and source_lineage.

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?

Provides explicit call conditions: 'Call when the user asks about infrastructure spending, construction capex, or public works momentum, or when timing infrastructure-linked equity, materials, or engineering exposure.' This is strong contextual guidance, though it does not mention when not to use the tool or name alternatives, so it misses the top tier.

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