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

With no annotations, the description takes the full burden and delivers: it discloses that condition names resolve despite physiological titles, that diagnosis-related topics contain evidence-rating notes, and that ADHD/autism notes explicitly state diet is not a treatment per NICE. It also instructs to reproduce notes verbatim rather than summarize, a critical behavioral trait.

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 dense paragraph that front-loads the core function and then adds necessary nuances about condition resolution and evidence notes. Every sentence contributes value, though the paragraph could be slightly restructured for scannability. It remains appropriately sized for the complexity.

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 tool with no output schema, the description explains what results will contain (nutrients, reasons, raw recipes, evidence notes) and how to handle them. It covers goal and condition inputs, omission behavior, and the critical note-reproduction rule. A minor gap is that it doesn't explicitly describe the shape of the output list, but the essential calling context is complete.

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 adds real semantics beyond the schema: it clarifies that the topic parameter accepts goals or named conditions, gives examples, states how condition resolution works, and explains the effect of omitting the argument. It does not add detail on include_recipes beyond the schema, but the extra semantic richness justifies a 4.

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 exactly what the tool does: 'What to eat for a health goal — the nutrients involved, why each one, and which raw recipes carry them.' It names a specific resource (health topics), a clear verb ('eat for'), and explains how condition names resolve, distinguishing it from generic recipe or food lookup tools.

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 gives an explicit trigger: 'Call this before answering "what should I eat for X".' It also specifies the omission behavior for listing all topics. It does not explicitly contrast with sibling tools like search_recipes or lookup_food, but the use-case framing makes the intended context clear.

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