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

With no annotations provided, the description carries the full burden, and it does well: it discloses normalization per 100 g, multi-source reconciliation, per-result source counts, and the ability to exclude thinly sourced outliers via min_sources. This is meaningful behavioral context beyond what the schema alone would reveal.

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 three sentences with the core ranking behavior front-loaded, followed by filtering and data-quality caveats. Every sentence earns its place and no schema details are redundantly repeated.

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?

For a moderate-complexity tool with no output schema, the description covers the essential call semantics: what is ranked, in what unit, over what food scope, how optional filters work, and what the result's source-count field means. An agent has enough information to invoke it correctly and interpret its output.

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 coverage is 100%, so the baseline is 3; the description enriches this by explaining 'per 100 g' as the ranking basis, giving concrete examples for nutrient and category, and clarifying min_sources as a mechanism for spotting or excluding weakly supported values. This adds real meaning beyond the parameter descriptions.

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 precise verb and resource: 'Rank whole plant foods by how much of a nutrient they provide per 100 g,' which fully states the operation and scope. It also frames the exact question the tool answers ('highest in vitamin C / iron / calcium') and distinguishes it from singleton food lookups by highlighting reconciled multi-source data.

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 clearly conveys when to use the tool: when the user wants a per-100g ranking of plant foods by a nutrient, optionally filtered by category. It does not explicitly name sibling alternatives or exclusion conditions, but the 'highest in...' framing and plant-food scope provide enough guidance for an agent to select it over search/lookup tools.

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