edit_sketch_dimension

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states that the tool triggers `design.computeAll()` before returning, which is a significant side effect. It also reveals that on `dim_not_found`, the response includes available dimensions, helping the agent recover. This is exactly the kind of behavior that is not inferable from schema and is well-disclosed.

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 with a clear first sentence stating the core purpose, followed by important behavioral warnings, error recovery info, and then a clean Args section. Every sentence adds value; no fluff. The formatting with sections and examples makes it easy to scan.

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?

Given the tool's moderate complexity (4 params, no output schema), the description is complete: it covers purpose, parameter details, side effects, error recovery, and post-conditions. It even references a known failure mode and a follow-up tool. There is no missing critical information for an agent to use it effectively.

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 compensate. It thoroughly explains each parameter: `sketch` (searches root + sub-components), `dim_name` (parameter name, find via `probe_sketch_dimensions`), `new_expression` (with detailed format rules: numeric literals, user param references, expressions, units required unless bare), and `component_name` (disambiguation). This far exceeds the minimal schema titles and provides essential semantic detail.

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 clearly states the tool changes a sketch dimension's expression by parameter name, with specific examples (e.g., `d278`). It distinguishes from siblings like 'add_dimension' (which adds dimensions) and 'probe_sketch_dimensions' (which reads them), and from parameter management tools like 'update_parameter'. The action is specific and unambiguous.

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 provides explicit when-to-use guidance: it mentions using `probe_sketch_dimensions` to find parameter names, and explicitly recommends running `audit_feature_health` after the edit to check for downstream breakage. It also mentions a specific known pitfall (fillets on moving edges, see G11). This gives the agent clear context on when and how to use it, and what to do after.

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