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

All annotations are false, so the description carries the full behavioral disclosure burden. It does so well by revealing active-context binding, the risk of logging a shot to the wrong bean, the taste-pending filing behavior for tag-less/rating-less shots, and the fact that the diagnosis engine consumes tags and metrics rather than free-text flavor_notes. This goes far beyond what the schema exposes.

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?

Four dense sentences, each carrying a distinct operational rule. It front-loads the core purpose and active-context behavior, then layers the exception cases. There is no filler; every sentence earns its place given the complexity of the tool.

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?

The description covers the critical decision points: active context overrides, explicit bean identification, backdating, taste reporting, and the clean-taste distinction. Combined with the output schema, this is enough for correct invocation in the main scenarios. It does not walk through secondary parameters like observations, data_confidence, temp_taste, or best_brew, but those are adequately covered by the input schema.

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?

With 65% schema coverage, the schema already documents many parameters. The description adds high-value meaning to bean_id, pulled_at, sensory_tags, tasted, and flavor_notes: bean_id must be forced when the user names a coffee, pulled_at enables backdating, and tasted=1 with no sensory_tags is the only correct way to record a clean shot. It does not clarify every remaining parameter, but it targets the ones most likely to cause incorrect behavior.

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 opens with a specific verb and resource: 'Log an espresso or alternative shot/brew attempt.' This clearly identifies the tool as a creation/logging operation. It does not explicitly contrast itself with siblings like update_shot or diagnose_shot, so it stops short of full sibling differentiation.

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 strong conditional usage rules: ALWAYS pass bean_id when the user names a coffee, use pulled_at for backdating, add sensory_tags when taste is reported, and pass tasted=1 for clean shots. These are excellent within-tool guidelines, but it never says when to prefer log_shot over a sibling tool such as update_shot or diagnose_shot, so it is not a 5.

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