bridge_facade_spacing

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

With no annotations provided, the description carries the full burden. It usefully discloses the key behavioral trait - that this is a pure calculation with no model mutation - but says nothing about permissions, determinism, or how the computed offsets are returned.

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?

A single front-loaded sentence with no filler; the verb, output, units, and safety constraint are all packed in without 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?

For a 5-parameter geometry tool with no annotations, no output schema, and zero schema descriptions, this is too thin. The agent cannot tell what length/width/count represent geometrically or what the calculation returns.

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 5 parameters, so the description must compensate and largely does not. It hints at units (millimeters) and that piers are involved, but never explains length, width, count, or the end_pier/min_pier defaults.

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+resource: 'Calculate explicit window offsets and piers in millimeters'. The phrase 'without mutating a model' implicitly separates it from the apply/mutate siblings, but no sibling is named, so differentiation is inferential rather than explicit.

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?

No when-to-use guidance, no prerequisites, and no named alternative. The agent must infer that this is a pre-planning calculation step from the non-mutating clause alone.

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