get_optimal_settings

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

With no annotations, the description carries the full burden; it does disclose that the tool reads recorded outcomes and returns three things (tuned settings, a success-likelihood read, and prioritized risks), and it flags the licensing gate. It does not clarify that the operation is non-mutating, nor what happens when the printer has no or sparse history, which are the key behavioral unknowns for a prediction tool.

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 useful content is front-loaded in the first two sentences, but the conditional framing ('Kiln Pro would read...') is awkward and partially restates the opening. The 'Upgrade: <URL>' line is marketing filler, though it does convey the tier requirement, so it is not pure waste.

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 read-only three-parameter prediction tool with no output schema, the description usefully enumerates the return contents. However, it leaves material-string expectations, behavior with insufficient history, and sibling selection unresolved, so an agent would still need to guess on several practical points.

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 only 33%, and the one documented field ('geometry_type') has a placeholder note ('Default: ""') rather than real semantics. The description adds nothing about printer_id, material (free-form string, no enum), or geometry_type, so it does not compensate for the coverage gap.

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 first sentence names a specific verb and resource ('Predict the best print settings') and scopes the basis ('based on historical success'), and the second sentence clarifies it reads this printer's recorded outcomes. That is clear enough for an agent to understand the function, but it never distinguishes itself from near-duplicate siblings such as predict_print_settings, recommend_settings, infer_print_settings, or get_auto_tuned_settings.

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 only guidance is the access prerequisite 'Requires Kiln Pro' plus an upgrade URL. There is no statement of when to prefer this tool over the many sibling settings-recommendation tools, and no exclusion or fallback behavior for users without Kiln Pro.

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