beaconed_bulk_optimize

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

Beyond the sparse annotations, the description discloses that the request is queued, returns HTTP 202, and is subject to a 10 req/min limit. These are meaningful behavioral details that help the agent understand the async and rate-limited nature of the operation.

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 short sentences convey the endpoint, purpose, async behavior, response status, and rate limit with no filler. Every part of the description contributes useful 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 two well-documented parameters and no output schema, the description is largely complete: it states the HTTP method, queued semantics, expected response, and rate limit. It could add how to monitor the submitted optimizations afterward, but that gap is minor given the sibling tools available.

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 description coverage is 100%, so the input schema already documents product_ids and fields thoroughly. The description adds no parameter-level detail beyond what the schema provides, so the baseline of 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?

The description names the specific action ('queue AI optimization'), the target resource ('multiple products'), and the batching mechanism ('in one request'). This clearly distinguishes it from the single-product optimization sibling and other product tools.

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 makes the intended use case explicit: optimizing multiple products in a single queued request. The rate-limit warning also signals that this is a constrained operation, but it does not explicitly name the single-product alternative or state when not to use it.

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