cst_matching_l_network

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

No annotations are present, so the description must handle behavioral disclosure. It explicitly states 'Pure Python computation — no CST connection needed,' which clarifies side effects (none) and independence from CST. It doesn't describe return format or error behavior, but the core behavior is well-covered.

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 three sentences, front-loaded with the primary purpose, and contains no redundant information. Every sentence adds value: purpose, computation, topologies, and independence from CST.

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 simplicity and no output schema, the description covers the essential aspects. It could mention that the tool returns L and C values, but this is implicit. Slight gap in output specification prevents a perfect score.

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?

All parameters have descriptions in the schema (100% coverage), so the description adds minimal extra meaning beyond contextualizing them as inputs for the matching network. The baseline 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?

The description clearly states it designs an L-section matching network, computes inductor and capacitor values, and supports lowpass/highpass topologies. It differentiates from sibling tools like cst_matching_pi_network and cst_matching_t_network by specifying 'L-section' and noting no CST connection.

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?

It indicates topologies supported but does not explicitly state when to use this versus alternatives like pi, T, or stub networks. However, the sibling list suggests usage for L-network design, and the description is clear enough for an agent to infer context.

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