fooddata_search

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

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds valuable behavioral context: it covers 300K+ foods including branded products, explains the four data types with examples, and notes that results include 'basic nutrient info.' This goes beyond the annotation by detailing content scope and result type, though it doesn't cover pagination or error behaviors.

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 concise and front-loaded. The first sentence states the purpose and output. The second paragraph efficiently explains the key data type enum values. Every sentence adds information, 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?

Given the rich schema (100% coverage, enums) and the read-only annotation, the description is nearly complete. It explains the tool's purpose, coverage, and data types. It doesn't explicitly describe pagination parameters, but the schema covers them, and the description's mention of 'basic nutrient info' hints at return content. The tool is well-suited for its role as a search endpoint.

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 parameters are already well-described. The description adds meaningful context for the dataType parameter by explaining what each enum value (Foundation, SR Legacy, Branded, Survey) means, which is not fully captured in the schema's brief 'Filter by data type.' It also clarifies the broad coverage of the search, aiding interpretation of query results.

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 clearly states the tool's action ('Search the USDA FoodData Central database for foods by keyword') and resource (USDA FoodData Central database). It also specifies the output scope ('Returns matching foods with basic nutrient info') and mentions coverage (300K+ foods including branded products). This distinguishes it from sibling tools like fooddata_detail and fooddata_list.

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 clear context that this is for keyword-based searching of foods, covering multiple data types. However, it does not explicitly mention when to use this tool versus alternatives (e.g., fooddata_detail or fooddata_list), nor does it state any exclusions or preconditions. Usage is implied but alternatives are not addressed.

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