fit_check

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

With no annotations to lean on, the description steps up by disclosing the return object's fields (fit_class, min/max/nominal clearance, prob_interference) and the underlying statistical assumption (normal model, half-band = 3-sigma), which is real behavioral context. It stops short of stating that this is a side-effect-free computation or how errors/ambiguous inputs are handled.

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?

Two dense sentences, front-loaded with the purpose followed by input shapes then return keys; nothing is wasted. The terse shorthand ('half-band = 3-sigma') is efficient but slightly compression-heavy for an agent with no other context.

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, so the description is the only source of return-value information, and it delivers the full result shape plus the probabilistic model. Inputs are also fully specified locally; the remaining gap is routing guidance against sibling tools and any statement of units or failure behavior.

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?

The schema declares both required parameters as untyped objects with additionalProperties=true and zero description coverage, so the description is doing all the work: it defines the two accepted shapes, {nominal, plus, minus} as signed deviations and {nominal, tol}. That fully compensates for the schema gap, with units and the meaning of 'nominal' left unstated.

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 description gives a specific verb and resource: 'Classify a hole/shaft pair', so the agent immediately knows this computes a fit classification. It does not, however, distinguish itself from close siblings such as fit_class or press_fit_stress, which is the only thing keeping it from a 5.

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?

There is no statement of when to use this tool versus the many overlapping siblings (fit_class, interference_check, min_clearance, tolerance_stackup). The agent must infer the intended context from the name and the input format alone; no exclusions or alternatives are named.

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