quick_buyOne-step purchase for products with instant checkout enabled (seller opt-in) and orders under bulk threshold. For products without instant checkout or larger orders, use request_quote instead.
| Name | Required | Description | Default |
|---|---|---|---|
| quantity | Yes | Number of units to purchase | |
| buyer_name | No | Buyer's full name | |
| product_id | Yes | Product ID (format: product_123) | |
| buyer_email | Yes | Buyer's email address | |
| buyer_company | No | Buyer's company name |
Changes observed during successful MCP inspections.
Input schema / properties / shipping_countryRemoved value: -{
- "description": "Two-letter country code (e.g., US, GB)",
- "type": "string"
-}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=true, covering double-charge risk and safety profile. The description adds useful context about the seller opt-in prerequisite and order-size threshold, but doesn't mention what happens on failure (e.g., payment handling, order creation side effects). With annotations carrying the primary behavioral burden, this is adequate but not rich.
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?
Two sentences, zero waste. The primary use case is front-loaded, and the alternative is presented immediately after.
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?
For a mutation tool with no output schema, the description covers the critical eligibility conditions and the fallback path. It omits return behavior (order confirmation details) and error modes, but given the annotations already signal non-idempotency and open-world reach, it is close to complete for correct invocation.
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?
Schema coverage is 100% with clear descriptions for all five parameters, so the schema does the heavy lifting. The description adds no parameter-level detail (e.g., units semantics, format constraints) beyond what the schema already declares. Baseline 3 is appropriate.
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 ('one-step purchase for products') with immediate scope qualifiers ('instant checkout enabled', 'orders under bulk threshold'). Clearly distinguishes itself from the named sibling request_quote.
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?
Explicitly defines when to use this tool (products with seller opt-in and small orders) and names the alternative (request_quote) for the disallowed conditions. Nothing is left to inference for an agent deciding between the two.
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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