cst_matching_quarter_wave

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

With no annotations provided, the description carries the burden of transparency. It discloses that computation is 'pure Python' (non-simulation based), which is helpful, but does not specify return values, constraints (e.g., frequency range validity), or side effects. More detail would improve transparency.

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 very concise: two sentences with no redundant phrases. It front-loads the core purpose, then mentions key features. Every word serves a purpose, earning a high score.

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 5 parameters, no output schema, and no annotations, the description is moderately complete. It covers design capabilities but omits important context such as return value structure (e.g., section impedances, lengths), edge cases (e.g., unmatched loads), or usage in a simulation workflow. For a design tool, this leaves gaps for an AI agent.

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 100% with clear parameter descriptions. The description adds minimal extra meaning beyond referencing 'maximally flat' and 'Chebyshev' (matching design enum) and 'single and multi-section' (matching num_sections). It does not elaborate on parameter semantics beyond what the schema provides, so baseline 3 is appropriate.

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 states the tool's purpose clearly: 'Design a quarter-wave transformer matching network.' It specifies supported features (single/multi-section, maximally flat/Chebyshev) and distinguishes it as a pure Python computation, setting it apart from sibling tools like cst_matching_stub or cst_matching_l_network.

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 implies usage for quarter-wave transformer design but does not explicitly state when to use it over other matching tools (e.g., stub, L-network). It lacks guidance on prerequisites (e.g., real impedances only) or alternatives, leaving the agent to infer context from the tool name and sibling list.

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