design_template

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

Annotations are absent, so the description carries the full disclosure burden. It does disclose useful behavior: the method parameter defaults to LRFD when left empty, the output destination path, and that design_validate checks cited clauses for existence. However, it never states whether the tool returns YAML text or writes a file — the phrase 'fill the produced skeleton and put it in' implies user placement but leaves the generation behavior ambiguous.

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 line breaks, bold highlights, and a ★ marker for the critical default-behavior warning. It is longer than average but every section earns its place: purpose, per-parameter semantics, and the downstream validation workflow. Nothing is redundant, though it could be trimmed slightly.

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 it has an output schema (so return values need no explanation) and 3 parameters all undocumented in the schema, the description covers parameters, default behavior, output destination, and the validation handoff. The only notable omission is an explicit statement of the tool's output form, which the output schema presumably supplies.

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?

With schema description coverage at 0%, the description must compensate, and it does: each parameter gets a Korean semantic label (member = 부재, shape = 단면, method = 설계법), concrete examples (휨부재, H형강), and a default-value warning for method. This meaningfully exceeds what the bare schema provides.

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 names a specific verb and resource: it produces a decision-tree YAML skeleton for a new member, and states the motivating problem (not being able to start when the format is unknown in an empty folder). This distinguishes it from siblings like design_flow and design_sheet by output type. It stops short of an explicit 5 because it never names a sibling tool it is not, relying on the workflow note referencing design_validate instead.

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?

Usage context is explicit: use it when starting a new member and the format is unknown. It also prescribes a concrete follow-up workflow (fill the skeleton, place it in ~/.kcsc-mcp/flows/, then run design_validate). The only gap is the absence of an explicit when-not-to-use statement or named alternative.

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