wb_stats_stocks

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

With no annotations at all, the description must carry the behavioral disclosure burden. It does add valuable operational detail: the 1000-result cap, the 100 default, and a business interpretation rule about stock without sales meaning overpaid storage. However, it does not disclose return shape, pagination behavior beyond the cap, or read-only status, which are meaningful gaps given there are no 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 dense sentence with a P0 priority marker. It packs scope, API limits, filtering, and an interpretive hint into minimal words with no filler. Every phrase earns its place and the most important scoping information is front-loaded.

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 simple two-parameter tool this is mostly usable, but there is no output schema and no annotations, so the description should say more about what a result looks like or how rows are structured. It also does not position the tool against the many stock/analytics siblings in the same family, leaving the agent to guess when this tool is the right choice.

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 description adds a cap and default for `limit`, which the schema alone does not fully document. But it introduces a serious ambiguity for `nm_ids`: the schema property is an integer array named nm_ids, while the schema description says 'offer_id filter' and the tool description says 'filters by article'. Articles/offer_ids are typically strings, so this conflicting guidance could cause an agent to pass incorrect values.

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 clearly identifies the resource as 'current stock' and scopes it to 'ALL WB warehouses', which distinguishes it from warehouse-specific stock tools. It also mentions the nm_ids filter, so an agent knows the tool covers both unfiltered and filtered stock queries. The lack of an explicit verb like 'returns' or 'lists' is minor because the intent is unambiguous.

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 useful operational constraints: API cap 1000, default 100, and filtering by nm_ids/article. However, it never names alternative tools or states when NOT to use this tool, so the agent has to infer how this differs from siblings like wb_stocks_get, wb_warehouse_remains, and wb_analytics_stocks.

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