get_print_learning_summary

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

With no annotations, the description carries full behavioral burden. It does disclose that the tool reads recorded outcomes and what it returns, but the conditional 'would read... and return' phrasing makes it ambiguous whether this describes live behavior or a gated upsell stub. Permissions, read-only status, and failure modes are not addressed.

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 core purpose is front-loaded in the first sentence and the return summary follows efficiently. The trailing upsell URL is a functional gating notice but slightly dilutes the definition's focus.

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?

With no output schema, the description usefully names what is returned (tuned settings, success-likelihood, priority risks), compensating for that gap. It remains thin on parameter meaning and on how the Kiln Pro gate behaves, but is largely complete for a read tool.

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 80%, above the threshold where the schema does the heavy lifting, so baseline 3 applies. The description adds nothing about material, failure_type, include_recovery, or include_community, leaving semantics entirely to the schema.

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?

States a specific verb and resource ('Get the current learning summary for a printer') and even enumerates the outputs (tuned settings, success-likelihood read, risks). However, it never distinguishes itself from close siblings like get_optimal_settings, get_print_confidence, or get_auto_tuned_settings, which cover overlapping territory.

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 usage signal is the prerequisite 'Requires Kiln Pro,' which is a gating condition rather than guidance on when to pick this tool. No alternatives or when-not-to-use conditions are given, leaving selection between the many tuning/confidence siblings to inference.

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