print_check

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

Beyond the readOnly/idempotent annotations, the description discloses the comparison logic, conservative default strengths, exclusion of stresses near fully fixed faces, separate peak reporting for support artefacts, and the screening limitation. This is rich behavioral context.

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 front-loaded sentences cover purpose, mechanics, strength defaults, exclusions, and the screening caveat. The description is dense but has no filler, and its length is justified by the analysis being described.

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?

There is no output schema, but the description covers what is computed and reported, including best orientation and separately reported peak stresses. The remaining gap is that optional parameters such as language and run_id are left undocumented.

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 description must carry parameter meaning. It explains the two strength values and their defaults, but does not name or clarify run_id or language, and only indirectly implies case_id.

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 analysis (3D-print strength check) and a specific input (solved linear static case), and the rest details layer-vs-filament comparisons and best-orientation output. This is clearly distinguishable from generic result or probe siblings.

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?

It establishes the post-solve screening context and explicitly warns 'Screening only: say so to the person.' It does not explicitly name when to use get_results, probe_results, or compare_runs instead, so the guidance is clear but lacks alternatives or exclusions.

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