predict_print_settings

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

No annotations are present, so the description carries the full burden. It discloses the core behavioral trait (prediction degrades gracefully from exact match to geometric similarity to material defaults) and explicitly warns about the v2 caveat with real consequences. It does not, however, mention whether the tool has side effects, requires prior state (like `record_print_dna` calls), or how errors are surfaced when no historical data exists.

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 description is well-structured: a one-sentence summary up front, a succinct algorithm note, then a clean Args block. Every sentence earns its place. It loses a small point for some awkward whitespace/indentation in the raw text and for spending its only prose sentences on already-obvious content while domain jargon like 'DNA data' goes unexplained.

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 8 parameters, no output schema, and no annotations, the description covers the algorithm and parameter semantics well. However, with 600+ siblings including extremely similar tools like `infer_print_settings`, `recommend_settings`, and `predict_print_failure`, the complete absence of any return-value description or disambiguation is a meaningful gap for an agent choosing purposes.

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 0%, so the docstring Args section is the sole documentation, and it fully compensates: all 8 parameters are documented with clear types, units (mm^2, mm^3), ranges (0.0-1.0), and the optional `geometric_signature_v2` receives a purpose-specific note. Slight deduction because there are no format examples or valid-combination guidance for the optional parameter.

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?

Uses a specific verb+resource ('Predict optimal print settings') and clarifies the prediction mechanism via a documented fallback chain (file hash → geometric similarity → material defaults). However, it doesn't differentiate itself from closely-named siblings like `infer_print_settings` or `recommend_settings`, and the 'historical DNA data' phrase is figurative and could be mildly confusing.

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?

Clearly implies when different data sources are used through the fallback ordering, and provides a crucial warning about passing `geometric_signature_v2` to avoid mis-sized predictions. But it never states when to prefer this tool over the many overlapping siblings, does not give exclusions, and prerequisites (e.g., linking to `fingerprint_model` output) are only implicit.

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