Pick a Serving (FatSecret)

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds meaningful context by revealing the 'fuzzy-matches' behavior and natural-language parsing, which are traits not captured by annotations.

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 a single, efficient sentence that front-loads the action and purpose without waste. Every word contributes to understanding.

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?

The description gives context for the use case ('before logging it') but omits critical runtime behavior: what happens when no serving matches, or whether it returns the matched serving object or an ID. Since there is no output schema, the description should clarify these aspects.

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?

With 0% schema description coverage, the description carries the burden. It implicitly maps 'food's servings' to foodId and 'natural-language quantity' to quantityDescription, but does not explain the meaning of the default empty string or what happens if quantityDescription is omitted. This is partial but not complete compensation.

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 a specific action: fuzzy-matching a natural-language quantity against a food's servings. It also explains the benefit (avoiding manual inspection of the servings array), which distinguishes it from sibling tools like getting a food's details or preparing an entry.

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 implies when to use it ('before logging it') and provides context about the workflow. However, it does not explicitly mention alternatives or exclude cases where it should not be used, such as when an exact serving ID is already known.

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