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

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds critical behavioral details: data comes from live bakery data (so results may change), sold-out items are omitted from the 'today' lane, and the explicit constraint never to quote items not in a result. These go beyond the annotations and help the agent avoid stale or hallucinated information.

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 two sentences with no wasted words. It front-loads the core purpose, then efficiently explains the two lanes and the safety rule. Every sentence carries necessary information, and the structure is easy to parse quickly.

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 that an output schema exists, the description doesn't need to explain return values. It covers the essential aspects: the data source (live), lane semantics, and a crucial usage constraint. It omits any mention of pagination or sorting, but those are likely handled by the output schema. The description is sufficient for an agent to call the tool correctly in most scenarios.

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?

The input schema already describes both parameters with 100% coverage, including the enum for lane and the optional section filter. The description adds value by explaining the real-world meaning of the lane values (same-day vs. catering) and the size options, which the schema does not convey. This enriches the agent's ability to select the correct parameter values.

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 that this tool retrieves the Pinoyshki Bakery menu with prices and photos from live data. It distinguishes between the 'today' and 'catering' lanes, giving the agent a precise understanding of what each returns. The instruction to never quote items not in a result further sharpens the purpose, making it distinct from a generic menu fetch.

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 for when to use each lane: 'today' for same-day pickup orders (excluding sold-out items) and 'catering' for advanced orders with specific sizes. It also includes a behavioral rule ('Never quote a price or item that is not in a result') that guides usage. However, it does not explicitly mention alternatives like search_menu or show_item, so the guidance is strong but not fully comparative.

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