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

Annotations already mark readOnlyHint=true; the description adds value by disclosing the update cadence ('Updates: monthly'), the return components (percentile rank, trend, top_drivers, FRED source_lineage), and the calculation basis. No behavioral contradictions are present.

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?

Two sentences pack the core output, methodology, use cases, and update frequency with no filler. The description is front-loaded and every clause earns its place.

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?

Despite no output schema, the description enumerates key return fields and the severity scale, and the schema covers the only parameter. For a read-only single-parameter tool, this is complete enough for an agent to select and invoke 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 schema itself fully explains the optional days parameter, including Gold tier requirements. The description adds no parameter-level detail, so baseline 3 applies.

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 and resource: 'Returns a 0-100 food price shock severity score' and details the exact blended methodology and output fields. This clearly distinguishes the tool as a specialized food-inflation indicator despite opaque sibling names.

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?

Explicit use cases are provided: 'Call when the user asks about food inflation, grocery or commodity price spikes, food security, or agricultural costs...' It lacks explicit when-not-to-use guidance or named alternatives, so it doesn't reach a 5.

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