gdt_check

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

No annotations exist, so the description carries the full burden, and it does disclose meaningful behavior: the diametral 2*hypot(x,y) offset computation, that mmc_bonus adds bonus tolerance, and the returned field set. It does not explicitly state that the tool is side-effect-free/read-only, but the checker framing makes this clear enough.

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 purpose in one sentence, then adds control enumeration, parameter guidance, and the return shape. The inline enum list is dense but necessary; every clause carries information with little waste.

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 and 0% schema description coverage, the description supplies the return structure and most parameter semantics on its own. Given the domain-specific GD&T complexity, it is nearly self-sufficient, missing only explicit meaning for zone and datum_refs.

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 coverage is 0%, so the description must compensate, and it explains four of six parameters with real semantic meaning: control (with the full enum), actual (measured deviation), offset (diametral form), and mmc_bonus. Only zone and datum_refs are left to inference, a minor residual 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 ('Check a measured feature against a GD&T tolerance zone') and immediately enumerates the exact set of controls it supports. This distinguishes it clearly from generic tolerance siblings like tolerance_stackup, fit_check, or envelope_check without opening their schemas.

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 description is rich on invocation semantics (which control values exist, when to pass offset={x,y}, what mmc_bonus does) but never states when to reach for this tool versus alternatives like tolerance_stackup or fit_check, nor any exclusions. Usage is implied rather than routed.

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