recommend_products

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

No annotations are provided, so the description carries the behavioral burden. It discloses two important non-obvious behaviors: only in-stock products are considered and conflicting products are flagged based on current routine ingredients. It still does not define the exact return shape or handling of no-match cases, but for a non-destructive recommendation tool it provides meaningful transparency.

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 main behavior is captured in one front-loaded sentence, followed by concise per-parameter arg descriptions. There is no marketing fluff or redundant repetition of the schema. Every line contributes necessary information.

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 three-parameter tool with no output schema, the description gives enough to invoke it correctly and understand the general result: recommended in-stock products with conflict flags. The main gap is the lack of an explicit return format, but the behavioral description narrows that gap considerably. It is adequate for practical selection and invocation.

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 0%, and the Args section compensates by describing each paramter in plain language. It gives a concrete skin_type example ('grasa'), shows that concerns is a list of concerns to address, and clarifies current_ingredients as active ingredients already in the client's routine. It could add allowed values or the effect of omitting current_ingredients, but it adds real semantic value beyond the schema titles.

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 action: find in-stock products matching skin type and concerns, then flag conflicts with the client's current routine. The 'end-to-end' framing plus the conflict-flagging step clearly differentiates it from siblings like search_products and check_ingredient_conflicts. It leaves no ambiguity about what the tool does.

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 phrase 'end-to-end recommendation' implies this tool combines product search and conflict checking, so an agent can infer when it is appropriate. However, it never explicitly says when to use this tool instead of search_products, find_alternatives, or check_ingredient_conflicts. There is no when-not-to-use guidance, so usage context is only implied.

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