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

Annotations already declare readOnlyHint and openWorldHint, and the description is consistent with them (no contradiction). It adds valuable behavior beyond annotations: it warns that the nutrient preview is 'not guaranteed complete,' explains the default dataType logic (SR Legacy vs Branded based on brandOwner), and notes that only Branded records carry a brandOwner. This transparency about data completeness and default behavior exceeds what annotations alone provide.

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 dense with information but not bloated. It front-loads the core purpose and return value, then moves to downstream tool usage and defaults. Each sentence contributes new guidance. Slightly long but efficient; a score of 4 reflects that it could be slightly tightened without losing essential details.

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 output schema exists and the description already notes the returned nutrient preview (energy, protein, fat, carbs) and FDC IDs, an agent has enough to understand the call result. The description also covers defaults and edge cases (UPC codes, brandOwner) that are not evident from the schema alone. Nothing needed for correct invocation is missing.

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 baseline is 3. The description adds meaningful enhancements: it explains how the query parameter can also accept UPC/GTIN codes, and it details the interaction between dataType and brandOwner (that setting brandOwner defaults dataType to ['Branded'] unless explicitly overridden). This goes beyond the schema's static field descriptions, justifying a score above baseline.

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 states a specific verb ('Search'), a precise resource ('USDA FoodData Central foods'), and the input ('by keyword'). It also explains what is returned (matching foods with FDC IDs and a nutrient preview), and explicitly distinguishes itself from sibling tools by naming usda_get_food and usda_compare_foods as follow-ups. This fully establishes the tool's unique role.

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 routing: 'Use the returned fdcId with usda_get_food for the full nutrient profile, or usda_compare_foods for side-by-side comparisons.' It also clarifies when to set dataType to ['Branded'] and how to use UPC/GTIN codes or brandOwner filters. These guidelines directly address selection against alternative tools and appropriate usage contexts, leaving no ambiguity.

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