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Propose purchase

propose_purchase

Record a pending purchase intent for human approval; no money moves, no receipt is created, and over-budget requests are flagged until approved.

Instructions

Record a pending purchase intent only. Moves no money, creates no receipt, approves no card, and does not contact Stripe or Link. Needs a later human approval via decide_proposal before anything settles. When a budget exists and the amount is above what remains, the proposal is stored with over_budget set and still spends nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skuNoSKU or product identifier
urlNoProduct or checkout URL
merchantYesMerchant or seller name
rationaleNoWhy the agent wants this
amount_usdYesPurchase amount in USD

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.4/5.0
Behavior5/5

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

With annotation coverage limited to generic hints (readOnlyHint=false, destructiveHint=false, idempotentHint=false), the description carries the real behavioral burden and does: it enumerates the negative guarantees (no money moved, no receipt, no card approval, no Stripe/Link contact), states the human-approval dependency, and discloses the over_budget edge-case behavior. This is exactly the side-effect disclosure a mutation 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.

Conciseness4/5

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

Three sentences, no filler, and the strongest constraint (intent-only, no money) is front-loaded ahead of the approval dependency and the edge case. The final over_budget sentence is edge-case detail that is useful but slightly beyond the minimum, keeping it short of a 5.

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?

No output schema exists, and the description compensates by describing the observable outcome (proposal stored, over_budget set when the amount exceeds remaining budget). Combined with the safety profile and the decide_proposal hand-off, an agent has everything needed to call it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so all five parameters are already documented in the schema and the description adds no format, range, or validation detail beyond it. Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Record a pending purchase intent') and immediately scopes it as intent-only, sharply distinguishing it from decide_proposal, which performs the actual approval. An agent can tell it apart from its siblings 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 Guidelines4/5

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

It is explicit that this records intent only and that a later human approval via decide_proposal is required before anything settles, which routes the agent to the correct next tool. It does not state an explicit when-not condition (e.g., when to skip proposing and use get_budget first), but the 'only' scoping is a clear usage boundary.

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