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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds value by specifying the exact content returned (queued jobs, counts, unused hooks, recent items), which is beyond what annotations provide. No contradictions.

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?

A single, dense sentence that front-loads the purpose ('Live production state') and lists all return components without any redundant phrasing. Every word earns its place.

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 1-parameter read-only tool, the description covers the essential output (jobs, counts, hooks, recent items) sufficiently. It lacks explicit response structure details, but given the simplicity and that annotations cover safety, nothing critical is missing.

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%—the only parameter 'brand' is fully described as 'Brand slug.' The tool description does not add extra meaning to the parameter, so the baseline of 3 applies.

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 states this is a live production status tool for a brand, listing specific data points (queued/recent jobs, video counts, unused hooks, recent items). It distinguishes itself from sibling tools like job_get or package_list by focusing on brand-level aggregated state.

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 phrase 'Live production state for a brand' provides clear context for when to use this tool—when an overview of a brand's pipeline is needed. It does not explicitly mention alternatives or exclusions, but the brand-scoped nature is evident from the description.

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