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

Goes well beyond the annotations: no customer data is passed for Shopify/Square, nothing exists on the shop until the buyer pays, Versum holds no card, stock is checked live and 'not in stock' is an `unavailable` error with no order created. It also spells out retry semantics via `idempotency_key` (`repeated: true`, same link, never a second order) and enumerates the possible `status` values.

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?

Front-loaded with the consent rule, which is good, but the consent requirement is stated twice (the 🔴 opener and the closing sentence), and the trailing quote of the server instructions adds bulk without new constraints. Dense platform-by-platform prose is efficient enough but not tight.

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?

No output schema exists, and the description compensates by naming the return fields (`order_id`, `status`, `checkout_url`) and every `status` value. For a multi-platform mutation tool, the risk profile, prerequisites, error case and return shape are all present.

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 60% schema coverage the description adds real meaning: lines reference offerings by card id and require `variant_id` only when the card lists several variants, `customer` is only needed on WhatsApp, and `note` is dropped on a Shopify cart link. `business_id` semantics (the `connected` + `order` card) are only partly covered.

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?

States a specific verb and resource plus the platform-specific mechanics: a Shopify checkout-link cart, a Square order with a payment link, or a WhatsApp message to the owner. It also implicitly separates ordering from adjacent siblings (reading, quoting, requesting, booking) so an agent can tell it apart from book_slot and request_to_owner.

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?

Gives an explicit precondition ('only after the user has explicitly agreed to this exact action') and a targeting rule ('For a business whose card shows an `order` action with method `connected`'). What's missing is naming the natural predecessor, quote_order, so the agent infers on its own that it should price with quote_order before placing.

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