get_chat_preprompts

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

No annotations are provided, so the description carries the transparency burden. It explicitly frames the operation as read-only ('Retrieves', 'Returns') and explains the contextual vs generic response behavior with examples. It does not discuss auth or rate limits, but for a non-mutating retrieval tool this is a minor gap.

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 compact and well-structured. Every sentence earns its place: the first states the core action, the second gives output examples, and the third identifies the primary use case. No redundant 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 tool's moderate complexity, all required parameters are documented, and the description covers the return value categories (contextual and generic preprompts) even without an output schema. It could explicitly state response formatting or pagination, but the overall description is sufficient for correct 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 coverage is 100%, placing the baseline at 3, but the description adds meaningful context: examples of page titles, clarification that GTIN applies only to product detail pages, and concrete examples of contextual vs generic modes. This helps an agent choose parameter values correctly.

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 uses a specific verb ('Retrieves') and identifies a precise resource: contextual and/or generic pre-suggested questions for the Carrefour AI chat assistant. It clearly differentiates from siblings like search_products or get_personalized_recommendations by focusing on chat preprompts and navigation context.

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 states when to use the tool: for populating chat suggestion chips on search results or product pages, based on the user's navigation context. It does not explicitly exclude alternatives, but the use case is clear and well-scoped.

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