get_stock_levelLive availability check for a SKU (is it available now and how many are in the London warehouse).
| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes | Product SKU exactly as shown on the site. |
| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes | ||
| locale | Yes | ||
| available | Yes | ||
| warehouse_quantity | Yes | Units in the London warehouse; null when not reported. |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "available": {
+ "type": "boolean"
+ },
+ "locale": {
+ "type": "string"
+ },
+ "sku": {
+ "type": "string"
+ },
+ "warehouse_quantity": {
+ "anyOf": [
+ {
+ "type": "number"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "description": "Units in the London warehouse; null when not reported."
+ }
+ },
+ "required": [
+ "sku",
+ "available",
+ "warehouse_quantity",
+ "locale"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool is read-only, non-destructive, and open-world. The description adds valuable behavioral context by emphasizing 'live' availability, meaning real-time inventory rather than cached data, and by scoping the check to the London warehouse. No contradiction exists between description and annotations.
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?
The description is a single well-structured sentence that front-loads the core purpose and immediately clarifies what the tool returns. There is no filler, repetition, or unnecessary background information.
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 tool with one required parameter, an existing output schema, and read-only annotations, the description is complete: it names the input type, the live nature of the check, and the specific warehouse scope. An agent has enough information to select and call the tool correctly.
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?
The schema describes the sku parameter completely, including min/max length and the instruction to use the SKU 'exactly as shown on the site.' With 100% schema description coverage, the tool description does not need to add much parameter detail, and it appropriately keeps the focus on behavior rather than repeating the schema.
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?
The description states a specific action (live availability check), resource (SKU), and the exact information returned (available now and quantity in the London warehouse). This clearly distinguishes it from sibling tools like check_shipping_availability or get_prices_for_country, which answer different questions.
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?
The description gives clear context for when to use the tool: when an agent needs real-time stock availability and quantity for a SKU in the London warehouse. It does not explicitly name alternatives or say when not to use it, so it falls short of a 5.
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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