cst_matching_stub

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

With no annotations, the description carries full burden. It discloses 'Pure Python computation' (no CST interaction) and that it computes stub length and distance, but does not clarify whether results are returned, printed, or applied to a project. More detail on side effects or output method 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?

Three short, focused sentences with no redundancy. The first sentence states the purpose, the second details the computation method, and the third notes the implementation. Every sentence adds value.

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?

The description omits output specification (units, format) and does not mention any prerequisites or return behavior. Given the 5 parameters and no output schema, the description should provide more details to ensure correct invocation.

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 100% (all parameters documented). The description adds context that outputs are stub length and distance, but does not enhance parameter understanding beyond the schema. Baseline score of 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?

Clearly states 'Design a single-stub impedance matching network' and specifies it computes stub length and distance. Differentiates from sibling tools that handle other matching types (L-network, quarter-wave, etc.). The phrase 'Pure Python computation' further clarifies it is a standalone calculation.

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 single-stub matching but does not explicitly state when to use this tool over alternatives like cst_matching_l_network or cst_matching_quarter_wave. No exclusions or when-not-to-use are provided.

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