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Glama

ue_place

Places an order only after explicit approval, using a one-time confirm token and rechecking the checkout snapshot and spend caps; refuses on mismatch or limit breach.

Instructions

Place the order. Requires the confirm_token from ue_review; the checkout snapshot is re-read and must still match (total / store / cart) and be within the per-order / per-day caps — otherwise it refuses.

Use this when:

  • The user has explicitly said yes to the exact review you showed them. Only then.

Notes:

  • If it comes back status unknown, or with any error after the gates passed, that does NOT mean it failed — the order may well have gone through. Check ue_orders first and tell the user what you find.

  • Never call ue_place again for the same cart: a second press is a second order and a second charge.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
confirm_tokenYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

No annotations exist, so the description carries the full burden and does so richly: it discloses the re-read validation gates (total/store/cart), the per-order and per-day caps, the refusal behavior, the idempotency hazard (a second press is a second charge), and crucially the ambiguous-failure semantics ('status unknown ... does NOT mean it failed'). That is exactly the context a mutating, money-moving tool needs.

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

Conciseness5/5

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

Front-loaded with the core action and precondition, then segmented into when-to-use and notes. Every sentence carries operational weight (gates, ambiguous failure, double-charge) with no filler.

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?

With no annotations and no output schema, the description still tells the agent everything needed to call it safely: prerequisite token source, what is validated server-side, what refusal means, and how to reconcile an uncertain result. Nothing material is missing.

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 0% and the single parameter is undocumented in the schema, so the description must compensate. It does: confirm_token is stated to come from ue_review and to be re-validated against the snapshot and caps. It stops short of describing format/expiry/lifetime, so not a 5.

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+resource ('Place the order') with unambiguous scope, and names the sibling it depends on (ue_review) and the sibling used to verify outcome (ue_orders). An agent can distinguish it from ue_review and ue_orders without opening any 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?

Explicit when-to-use gate ('The user has explicitly said yes to the exact review you showed them. Only then.'), plus an explicit when-not ('Never call ue_place again for the same cart'). Alternatives and preconditions are named, not implied.

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