cst_matching_t_network

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

No annotations provided. States 'Pure Python computation' indicating no side effects. However, doesn't disclose return format, error handling, or prerequisites.

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?

Two concise sentences, front-loaded with key information. No 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?

No output schema, but description is adequate for a simple computation tool. However, it doesn't mention return values or integration with CST model, which could be important for the 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 has 100% coverage with descriptions for all 6 parameters. The description adds minimal extra meaning beyond the schema, just mentioning the L-C-L topology.

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 it designs a T-section impedance matching network (L-C-L) and distinguishes from Pi-network. Mentions it's pure Python, no simulation.

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?

Mentions it's the dual of Pi-network, but doesn't explicitly guide when to choose this over Pi or other matching networks. Lacks when-to-use or when-not-to-use scenarios.

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