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RoboTrunk

Read back an order

get_order
Read-onlyIdempotent

Reads back a store's order: the status and the totals as the store recorded them, in the store's own currency, and whether the store's own record is behind them (order_source, merchant_read). Use after the person has paid at the store and you want to check the order exists and read it back. Pass order_id or checkout_id. A store checkout_id reads that checkout's state while it has no order: a cancelled or expired checkout answers with its status and the message no_order_placed, saying no order was placed, and one still waiting on the person says no order has been confirmed yet, so call again in a moment; once the person has paid at the store's page, the store reports the order on a read of its checkout: the answer is status completed with the message order_confirmed ("The store confirmed order #1019.") and the store's order number in resource.order.label (resource.label on the order), shown only when the store reports one; resource.order.local_order_id is the order_id to read the order itself with (order_source, merchant_read, and order_from_checkout when the store's own order record was not read for it). Shipping, cancellation and refund after that are not shown. Not for placing or approving an order. Error recovery: identifier_required means pass order_id or checkout_id; not_found means no order can be read with that id, because over /mcp an order is read only with the order_id or checkout_id exactly as an earlier answer returned it, so call submit_feedback if the person did confirm, then start again with search_catalog; run_authority_missing or RUN_AUTH_REQUIRED on an id you were given means the run that owns the order has ended and no retry reaches it, so call submit_feedback; continuation_not_found means that reference is unknown, expired (references last up to 24 hours) or from another environment, so call search_catalog; rate_limited means wait 60 seconds; network_error means call again; any other error, including tool_layer_failure: retry once, then call submit_feedback. Next: submit_feedback to say what was unclear. It also reads RoboTrunk's test demo order (merchant null): pass that checkout_id while the person decides. Full guide: get_tool_guide or /agent/tools.json#get_order Example call: {"checkout_id":"the checkout_id create_checkout returned"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
order_idNoThe order id from `/orders/{order_id}` after the person confirms, or null to look it up from `checkout_id`.
checkout_idNoThe `checkout_id` from `create_checkout`, or null when you already hold `order_id`. At least one of the two must be non-null.
shopping_session_idNoOptional server-issued shopping session correlation ID. It is not purchase authority.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations it discloses the read-back currency and provenance (order_source, merchant_read), the full checkout state machine (no_order_placed, no order confirmed yet, order_confirmed with message and label), that references expire in 24 hours, and a 60-second rate-limit wait. This is far richer than the structured fields provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose and trigger, so the opening earns its place, but the middle and tail are a sprawling run-on with hard-to-parse parentheticals and interleaved error taxonomy. It is closer to over-stuffed than tight, and the structure taxes reading more than it needs to.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with an ambiguous dual-identifier lookup and no output schema, the description covers return shape (status, totals, messages, labels), the checkout-vs-order distinction, and full error recovery. An agent has everything needed to call it and interpret the result without the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% so the baseline is 3, but the description adds real semantics the schema lacks: which of order_id/checkout_id to pass and why, and that resource.order.local_order_id is the order_id to re-read with. The parenthetical labeling (order_source, merchant_read, order_from_checkout) is dense but adds mapping meaning above the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Reads back a store's order: the status and the totals') and explicitly distinguishes itself from the write siblings ('Not for placing or approving an order'). An agent can separate this from complete_checkout/create_checkout without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit trigger ('after the person has paid at the store and you want to check the order exists'), a negative case ('Not for placing or approving an order'), and routes to specific siblings (submit_feedback, search_catalog) for each recovery path. Nothing is left to inference.

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

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