moldability_screen

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

With no annotations, the description carries the full burden and does so richly: it discloses no-solver/no-geometry limits, the analytic logics (uniformity_ratio thresholds warn>2/fail>3, sink lobe detection, t∝s² cooling, S_linear=alpha·ΔT, cavity_scale_factor), graceful degradation when corpus fields are missing, and the full return shape. This is unusually complete behavioral disclosure for a mutation-free analytic 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?

Front-loads the tool's identity and scope, then uses numbered checks and a return-key list, so structure is strong. It is dense and jargon-heavy but nearly every clause conveys the analytic behavior; the enumeration could be slightly tighter.

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 9-parameter analytic tool with no output schema and no annotations, the description covers purpose, mechanics, return keys and escalation well enough to call it correctly. The main omission is the meaning of the three unexplained thermal/shrinkage override parameters.

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% across 9 params, so the description must compensate. It explains material, wall_samples and nominal_mm, and indirectly encodes warn_ratio/fail_ratio and sink_factor via their default values, but alpha_per_k, t_ambient_c and t_solidify_c go entirely unexplained, leaving a meaningful 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?

States a specific verb and resource ('Moldability DFx screen') and scopes it precisely with 'NO solver, NO geometry — fast analytic gate', which clearly distinguishes it from solver-based siblings like molding_fill_submit, molding_screen and moldability_check. The two named checks (wall-thickness quality, shrinkage) define exactly what it computes.

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?

Gives clear conditions for use and names the escalation path ('Low-fidelity gate: escalate_to=molding_fill_submit'), plus the input condition ('Pass wall_samples and/or nominal_mm, plus material'). It does not explicitly state when NOT to use it or contrast against the very similar moldability_check sibling, so it stops short of the top score.

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